Analyte data processing, reporting, and visualization

ABSTRACT

Certain aspects of the present disclosure provide techniques for processing and presenting analyte data. Some example aspects may describe techniques for generating and providing a user interface view of a user&#39;s performance report for display. Some example aspects may describe techniques for providing one or more user interface views for display on one or more widgets.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Application No.63/153,524 filed Feb. 25, 2021, which is hereby assigned to the assigneehereof and hereby expressly incorporated by reference herein in itsentirety as if fully set forth below and for all applicable purposesBACKGROUND

FIELD OF THE DISCLOSURE

Aspects of the present disclosure generally relate to continuous analytemonitoring and, more specifically, to analyte data processing,reporting, and visualization.

DESCRIPTION OF RELATED ART

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin. In a diabetic state, a person suffering from highblood sugar may experience an array of physiological side effectsassociated with the deterioration of small blood vessels. These sideeffects may include, for example, kidney failure, skin ulcers, bleedinginto the vitreous of the eye, and the like. A hypoglycemic reaction,such as a low blood sugar event, may be induced by an inadvertentoverdose of insulin, or after a normal dose of insulin orglucose-lowering agent. In a severe hypoglycemic reaction, there may bea high risk for headache, seizure, loss of consciousness, and coma.

A diabetic person may carry a self-monitoring blood glucose (SMBG)monitor which typically requires the user to prick his or her finger tomeasure his or her glucose levels. Given the inconvenience associatedwith traditional finger pricking methods, it is unlikely that a diabeticwill take a timely SMBG measurement and, consequently, may be unawarewhether his or her blood glucose value is indicative of a dangeroussituation.

A variety of non-invasive, transdermal (e.g., transcutaneous) and/orimplantable electrochemical sensors are being developed for detectingand/or quantifying blood glucose values. These devices generallytransmit raw or minimally processed data for subsequent analysis at aremote device. The remote device may have a display that presentsinformation to a user hosting the sensor. In some systems, a patient maycheck his or her glucose level on a hand held computing device. Thereare challenges to presenting this information discreetly and reliably.Moreover, there are challenges to efficiently analyze this informationsuch that reports and insights may be presented to the diabetic user forcontinuous management of the diabetic condition.

SUMMARY

The systems, methods, and devices of the disclosure each have severalaspects, no single one of which is solely responsible for its desirableattributes. Without limiting the scope of this disclosure as expressedby the claims which follow, some features will now be discussed briefly.After considering this discussion, and particularly after reading thesection entitled “Detailed Description” one will understand how thefeatures of this disclosure provide advantages that include improvedanalysis and presentation of analyte data.

Methods, means for, apparatus, processors and/or processing systems, andcomputer-readable mediums and/or computer program products, are providedfor processing and visualization of analyte data.

Certain aspects of the present disclosure provide a method forgenerating a user interface view associated with sensor datarepresentative of a glucose concentration level in a host. The methodmay include accessing sensor data, where the sensor data may include aplurality of blood glucose readings associated with the host during aplurality of analysis time periods in a current week. Each blood glucosereading may be indicative of a blood glucose concentration level of thehost at a respective time. The method may include determining an averageblood glucose concentration level of the host for the current week basedon the plurality of blood glucose readings. The method may includegenerating a performance report. The performance report may include, butis not limited to including, the average blood glucose concentrationlevel of the host for a first time period and a comparison of theaverage blood glucose concentration level of the host for the first timeperiod to average blood glucose concentration levels of the host for atleast two previous time periods of similar duration, a per-day averageblood glucose concentration level of the host, per-day percentages ofblood glucose concentration level of the host at one or more bloodglucose concentration level ranges, or a combination thereof. Theinformation in the report may be customizable by the user. The methodmay include generating a user interface view of the performance report.The method may include providing the user interface view of theperformance report for display.

Certain aspects of the present disclosure provide a method forgenerating a user interface view associated with sensor datarepresentative of a glucose concentration level in a host. The methodmay include accessing first data associated with blood glucoseconcentration level of a host, the first data associated being with afirst time period. The method may include analyzing the first data togenerate a first one or more user interface views associated with thefirst data for display on one or more widgets. The method may includeproviding the first one or more user interface views for display on theone or more widgets. The method may include automatically updating thefirst one or more user interface views for display on the one or morewidgets. Automatically updating the first one or more user interfaceviews may include accessing second data associated with blood glucoseconcentration level of the host, the second data being associated with asecond time period, analyzing the second data to generate a second oneor more user interface views associated with the second data for displayon the one or more widgets, and providing the second one or more userinterface views for display on the one or more widgets. The one or morewidgets may be customizable by the user.

It is to be understood that both the foregoing general description andthe following detailed description are example and explanatory only andare not restrictive. Further features and/or variations may be providedin addition to those set forth herein. For example, the aspectsdescribed herein may be directed to various combinations andsubcombinations of the disclosed features and/or combinations andsubcombinations of several further features disclosed below in thedetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above-recited features of the presentdisclosure can be understood in detail, a more particular description,briefly summarized above, may be had by reference to aspects, some ofwhich are illustrated in the drawings. It is to be noted, however, thatthe appended drawings illustrate only certain typical aspects of thisdisclosure and are therefore not to be considered limiting of its scope,for the description may admit to other equally effective aspects.

FIG. 1 is a diagram conceptually illustrating an example systemincluding an example continuous analyte sensor with sensor electronics,in accordance with some example aspects of the present disclosure.

FIG. 2 is a block diagram conceptually illustrating a sensor electronicsmodule communicating with multiple sensors, in accordance with someexample aspects of the present disclosure.

FIGS. 3A and 3B illustrate different views of a sensor system includinga mounting unit and sensor electronics attached thereto, in accordancewith some example aspects of the present disclosure.

FIG. 4 is a block diagram conceptually illustrating an analyteprocessing system, in accordance with some example aspects of thepresent disclosure.

FIG. 5 illustrates an example user interface view associated with sensordata representative of glucose concentration level(s) in a host, inaccordance with some example aspects of the present disclosure.

FIG. 6 is a flow diagram illustrating example operations for generatinga user interface view associated with sensor data representative ofglucose concentration level(s) in a host, in accordance with someexample aspects of the present disclosure.

FIG. 7 illustrates an example wireframe of a user interface view in avertical layout, in accordance with some example aspects of the presentdisclosure.

FIG. 8A illustrates an example wireframe of a user interface view in ahorizontal layout, in accordance with some example aspects of thepresent disclosure.

FIGS. 8B-8G illustrate enlarged views of features one through six shownin the vertical and horizontal wireframes of FIG. 7 and FIG. 8A,respectively, in accordance with some example aspects of the presentdisclosure.

FIG. 9 illustrates an example user interface view associated with sensordata representative of glucose concentration level(s) in a host in avertical layout which corresponds to the wireframe of FIG. 7, inaccordance with some example aspects of the present disclosure.

FIG. 10A illustrates an example user interface view associated withsensor data representative of glucose concentration level(s) in a hostin a horizontal layout which corresponds to the wireframe of FIG. 8A, inaccordance with some example aspects of the present disclosure.

FIGS. 10B-10G illustrate enlarged views of features one through sixshown in the vertical and horizontal user interface views of FIG. 9 andFIG. 10A, respectively, in accordance with some example aspects of thepresent disclosure.

FIG. 11A illustrates an example wireframe of another example userinterface view in a vertical layout, in accordance with some exampleaspects of the present disclosure.

FIG. 11B illustrates an example wireframe of another user interface viewin a horizontal layout, in accordance with some example aspects of thepresent disclosure.

FIGS. 11C-11F illustrate enlarged views of features one through fourshown in the vertical and horizontal wireframes of FIG. 11A and FIG.11B, respectively, in accordance with some example aspects of thepresent disclosure.

FIG. 12A illustrates an example user interface view associated withsensor data representative of glucose concentration level(s) in a hostin a vertical layout which corresponds to the wireframe of FIG. 11A, inaccordance with some example aspects of the present disclosure.

FIG. 12B illustrates an example user interface view associated withsensor data representative of glucose concentration level(s) in a hostin a horizontal layout which corresponds to the wireframe of FIG. 11B,in accordance with some example aspects of the present disclosure.

FIGS. 12C-12G illustrate enlarged views of features one through fourshown in the vertical and horizontal user interface views of FIG. 12Aand FIG. 12B, in accordance with some example aspects of the presentdisclosure.

FIG. 13A illustrates an example wireframe of another example userinterface view in a vertical horizontal layout, in accordance with someexample aspects of the present disclosure.

FIG. 13B illustrates an example wireframe of another example userinterface view in a horizontal layout, in accordance with some exampleaspects of the present disclosure.

FIGS. 13C-13E illustrate enlarged views of features one through threeshown in the vertical and horizontal wireframes of FIG. 13A and FIG.13B, respectively, in accordance with some example aspects of thepresent disclosure.

FIG. 14A illustrates an example user interface view associated withsensor data representative of glucose concentration level(s) in a hostin a vertical layout which corresponds to the wireframe of FIG. 13A, inaccordance with some example aspects of the present disclosure.

FIG. 14B illustrates an example user interface view associated withsensor data representative of glucose concentration level(s) in a hostin a horizontal layout which corresponds to the wireframe of FIG. 13B,in accordance with some example aspects of the present disclosure.

FIGS. 14C-14F illustrate enlarged views of features one through fourshown in the vertical and horizontal user interface views of FIG. 14Aand FIG. 14B, respectively, in accordance with some example aspects ofthe present disclosure.

FIG. 15 is a block diagram conceptually illustrating a softwarearchitecture for implementing widget functionality, in accordance withsome example aspects of the present disclosure.

FIG. 16 illustrates an example dashboard including a number of userinterface elements, also referred to herein as “widgets”, in accordancewith some example aspects of the present disclosure.

FIG. 17 is a flow diagram illustrating example operations for generatinga user interface view associated with sensor data representative ofglucose concentration level(s) in a host, in accordance with someexample aspects of the present disclosure.

FIG. 18 is a table categorizing example analyte data widgets, inaccordance with some example aspects of the present disclosure.

FIG. 19 illustrates a wireframe of an example summary widget, inaccordance with some example aspects of the present disclosure.

FIG. 20 illustrates a wireframe of another example summary widget, inaccordance with some example aspects of the present disclosure.

FIG. 21 illustrates another example summary widget, in accordance withsome example aspects of the present disclosure.

FIG. 22 illustrates another example summary widget, in accordance withsome example aspects of the present disclosure.

FIG. 23 illustrates another example summary type widget, in accordancewith some example aspects of the present disclosure.

FIG. 24 illustrates another example summary type widget, in accordancewith some example aspects of the present disclosure.

FIGS. 25A and 25B illustrates another example summary type widget, andits corresponding wireframe, respectively, in accordance with someexample aspects of the present disclosure.

FIGS. 26A and 26B illustrate another example summary type widget, andits corresponding wireframe, respectively, in accordance with someexample aspects of the present disclosure.

FIG. 27 illustrates a wireframe of another example summary type widget,in accordance with some example aspects of the present disclosure.

FIG. 28 illustrates a wireframe of another example summary type widget,in accordance with some example aspects of the present disclosure.

FIG. 29 illustrates a wireframe of another example summary type widget,in accordance with some example aspects of the present disclosure.

FIG. 30 illustrates a wireframe of another example summary type widget,in accordance with some example aspects of the present disclosure.

FIG. 31 illustrates a wireframe of another example summary type widget,in accordance with some example aspects of the present disclosure.

FIG. 32 illustrates another example summary type widget, in accordancewith some example aspects of the present disclosure.

FIGS. 33A and 33B illustrate an example motivation type widget, and itscorresponding wireframe, respectively, in accordance with some exampleaspects of the present disclosure.

FIGS. 33C-33E illustrate the example motivation type widget illustratedin FIG. 33B with additional features, in accordance with some exampleaspects of the present disclosure.

FIG. 34 illustrates another example motivation type widget, inaccordance with some example aspects of the present disclosure.

FIGS. 35A and 35B illustrate another example motivation type widget, andits corresponding wireframe, respectively, in accordance with someexample aspects of the present disclosure.

FIG. 36 illustrates another example motivation type widget, inaccordance with some example aspects of the present disclosure.

FIG. 37 illustrates a wireframe of another example motivation typewidget, in accordance with some example aspects of the presentdisclosure.

FIG. 38 illustrates another example motivation type widget, inaccordance with some example aspects of the present disclosure.

FIG. 39 illustrates another example motivation type widget, inaccordance with some example aspects of the present disclosure.

FIG. 40 illustrates another example motivation type widget, inaccordance with some example aspects of the present disclosure.

FIG. 41 illustrates another example motivation type widget, inaccordance with some example aspects of the present disclosure.

FIG. 42 illustrates another example motivation type widget, inaccordance with some example aspects of the present disclosure.

FIG. 43 illustrates a wireframe of another example motivation typewidget, in accordance with some example aspects of the presentdisclosure.

FIG. 44 illustrates a wireframe of an example event type widget, inaccordance with some example aspects of the present disclosure.

FIG. 45 illustrates a wireframe of another example event type widget, inaccordance with some example aspects of the present disclosure.

FIG. 46 illustrates another example event type widget, in accordancewith some example aspects of the present disclosure.

FIGS. 47A and 47B illustrate another example event type widget, and itscorresponding wireframe, respectively, in accordance with some exampleaspects of the present disclosure.

FIG. 48 illustrates another example event type widget, in accordancewith some example aspects of the present disclosure.

FIG. 49 illustrates another example event type widget, in accordancewith some example aspects of the present disclosure.

FIG. 50 illustrates another example event type widget, in accordancewith some example aspects of the present disclosure.

FIG. 51 illustrates another example event type widget, in accordancewith some example aspects of the present disclosure.

FIG. 52 illustrates a wireframe of an example other type widget, inaccordance with some example aspects of the present disclosure.

FIGS. 53A and 53B illustrate another example other type widget, and itscorresponding wireframe, in accordance with some example aspects of thepresent disclosure.

FIG. 54 illustrates another example other type widget, in accordancewith some example aspects of the present disclosure.

FIG. 55 illustrates a wireframe of another example other type widget, inaccordance with some example aspects of the present disclosure.

FIG. 56 is a flow diagram illustrating example operations foractivating, customizing, and using a dashboard with widgets, inaccordance with some example aspects of the present disclosure.

FIG. 57 illustrates an example user interface that may include variouswidgets, in accordance with some example aspects of the presentdisclosure.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures. It is contemplated that elements disclosed in one aspectmay be beneficially utilized on other aspects without specificrecitation.

DETAILED DESCRIPTION

Data visualization is the process of translating large data sets andmetrics into charts, graphs, and other visuals. The resulting visualrepresentation of data makes it easier to identify and/or sharereal-time trends, outliers, and new insights about the informationrepresented in the data. Perhaps one of the most pivotal advantages ofdata visualization is that digital visualization facilitates the easyand quick assimilation of large amounts of data. Visualization allowsanalysts and end users to recognize patterns and relationships in largevolumes of data that may not be easily seen in raw data or reports. Thismay help in identifying emerging trends, for example, to allow a user toaddress health issues before they become bigger problems. The goal is toprovide actionable insights that help drive change. For ease ofexplanation, a user herein, may refer to a patient (also referred to asa host); however, a user may refer to the patient's caregiver, or anyother individual with an interest in the patient's well-being.

Health data visualization is increasingly a major focus in healthanalytics. Visualizing health data is a powerful way to share urgenthealth information swiftly and effectively. When implemented correctly,health data visualization may provide numerous benefits to an end user.For example, data visualization tools may promote the improvedabsorption of health information, provide quick access to meaningfulhealth insights, communicate findings in a constructive way to engageand inform users of future and/or current health issues, and encourageuser interaction with data to make informed lifestyle decisions andchanges to stimulate a healthy lifestyle.

There is a growing emphasis on self-monitoring applications that allowpatients to measure their own physical health parameters. Datavisualization techniques and tools play an important role in thecontinuous management of one's health. For example, data visualizationtechniques and tools may be used to efficiently analyze, report, andprovide insights to a diabetic user for continuous management of adiabetic condition.

Aspects of the present disclosure provide apparatus, methods, processingsystems, and computer-readable mediums for analyte data processing,reporting, and visualization.

The term “analyte” as used herein is a broad term used in its ordinarysense, including, without limitation, to refer to a substance orchemical constituent in a biological fluid (for example, blood,interstitial fluid, cerebral spinal fluid, lymph fluid or urine) thatcan be analyzed. Analytes can include naturally occurring substances,artificial substances, metabolites, and/or reaction products. Analytesfor measurement by the devices and methods may include, but may not belimited to, potassium, glucose, acarboxyprothrombin; acylcarnitine;adenine phosphoribosyl transferase; adenosine deaminase; albumin;alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle),histidine/urocanic acid, homocysteine, phenylalanine/tyrosine,tryptophan); androstenedione; antipyrine; arabinitol enantiomers;arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactiveprotein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholicacid; chloroquine; cholesterol; cholinesterase; conjugated 1-βhydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MMisoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine;dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcoholdehydrogenase, alpha 1-antitrypsin, glucose-6-phosphate dehydrogenase,hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E,hemoglobin F, D-Punjab, hepatitis B virus, HCMV, HIV-1, HTLV-1, MCAD,RNA, PKU, Plasmodium vivax, 21-deoxycortisol); desbutylhalofantrine;dihydropteridine reductase; diptheria/tetanus antitoxin; erythrocytearginase; erythrocyte protoporphyrin; esterase D; fattyacids/acylglycines; free 3-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; glucose-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I;17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sisomicin; somatomedin C; specificantibodies recognizing any one or more of the following that may include(adenovirus, anti-nuclear antibody, anti-zeta antibody, arbovirus,Aujeszky's disease virus, dengue virus, Dracunculus medinensis,Echinococcus granulosus, Entamoeba histolytica, enterovirus, Giardiaduodenalisa, Helicobacter pylori, hepatitis B virus, herpes virus,HIV-1, IgE (atopic disease), influenza virus, Leishmania donovani,leptospira, measles/mumps/rubella, Mycobacterium leprae, Mycoplasmapneumoniae, Myoglobin, Onchocerca volvulus, parainfluenza virus,Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa, respiratorysyncytial virus, rickettsia (scrub typhus), Schistosoma mansoni,Toxoplasma gondii, Trepenoma pallidium, Trypanosoma cruzi/rangeli,vesicular stomatis virus, Wuchereria bancrofti, yellow fever virus);specific antigens (hepatitis B virus, HIV-1); succinylacetone;sulfadoxine; theophylline; thyrotropin (TSH); thyroxine (T4);thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins, and hormones naturally occurring in blood or interstitialfluids can also constitute analytes in certain implementations. Ions area charged atom or compounds that may include the following (sodium,potassium, calcium, chloride, nitrogen, or bicarbonate, for example).The analyte can be naturally present in the biological fluid, forexample, a metabolic product, a hormone, an antigen, an antibody, an ionand the like. Alternatively, the analyte can be introduced into the bodyor exogenous, for example, a contrast agent for imaging, a radioisotope,a chemical agent, a fluorocarbon-based synthetic blood, a challengeagent analyte (e.g., introduced for the purpose of measuring theincrease and or decrease in rate of change in concentration of thechallenge agent analyte or other analytes in response to the introducedchallenge agent analyte), or a drug or pharmaceutical composition,including but not limited to exogenous insulin; glucagon, ethanol;cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrousoxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons);cocaine (crack cocaine); stimulants (amphetamines, methamphetamines,Ritalin, Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbiturates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body can also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and5-Hydroxyindoleacetic acid (FHIAA), and intermediaries in the CitricAcid Cycle.

While the analyte for measurement and visualization by the devices andmethods described herein is glucose, other analytes listed, but notlimited to, above may be considered. Biological parameters, such as bodytemperature, heart rate, metabolic function, respiratory rate, and thelike, may be also be considered.

Aspects provide for a report that may be generated by a processor basedon analyte data, for example, continuous glucose monitoring (CGM) data.The report may also be generated based on information input from a userand/or other information. The report may include information, such as anaverage analyte concentration level of the user for the current week anda comparison of the average analyte concentration level of the user forthe current week to analyte concentration levels of the user for atleast two previous weeks, and/or a per-day average analyte concentrationlevel of the user, per-day percentages of analyte concentration levelsof the user at one or more analyte concentration level ranges. Theinformation in the report may be customizable by the user. Aspects ofthe disclosure provide for various data associated with continuousanalyte monitoring that can be processed and used to generate one ormore user views that may be presented on one or more widgets to a user.The information and user views can be updated based on the continuouslymonitored data and/or based on other parameters. The widgets may includesummary type widgets, motivation type widgets, event type widgets,and/or other widgets. The widgets may also be customizable by a user.

FIG. 1 is a diagram conceptually illustrating an example system 100including an example continuous analyte sensor with sensor electronics,in accordance with certain aspects of the present disclosure. Forexample, the system 100 may be configured to provide a report, based oncontinuously monitored analyte data, in accordance with certain aspectsdiscussed in more detail with respect to FIGS. 5-14. The system 100 maybe configured to provide one or more widgets based on continuouslymonitored analyte data, in accordance with certain aspects discussed inmore detail herein with respect to FIGS. 15-55.

The system 100 includes a continuous analyte sensor system 8 includingsensor electronics 12 and a continuous analyte sensor 10. The system 100may include other devices and/or sensors, such as a medicament deliverypump 2 and a glucose meter 4. The continuous analyte sensor 10 may bephysically connected to a sensor electronics 12 and may be integral with(e.g., attached and non-releasable) or releasably attachable to thecontinuous analyte sensor 10. The sensor electronics 12, medicamentdelivery pump 2, and/or glucose meter 4 may couple with one or moredevices, such as display devices 14, 16, 18, and/or 20.

In some aspects, the system 100 may include an analyte processor 490configured to analyze analyte data (and/or other patient related data)provided via network 406 (e.g., via wired, wireless, or a combinationthereof). As later shown in FIG. 4, in certain embodiments, analyteprocessor 490 is a cloud-based processor that is configured to analyzeanalyte data received through network 406 from continuous analyte sensorsystem 8 and/or other devices, such as display devices 14, 16, 18,and/or 20 and the like, associated with the user and generate reportsproviding high-level information, such as statistics, regarding themeasured analyte over a certain time frame. In embodiments where theanalyte processor 490 is a cloud-based processors, the generated reportsmay be provided, in the form of user interface (UI) views, to UIs (e.g.,UIs 410A-C of FIG. 4) of display devices 14, 16, 18, and/or 20.

Although not shown in FIG. 4, in certain embodiments, the analyteprocessor may be one of display devices 14, 16, 18, or 20 that isconfigured to analyte data received from continuous analyte sensorsystem 8 and/or other devices, such as the other display devices. Insuch embodiments, analyte processor 490 receives analyte data (e.g.,directly) from continuous analyte sensor system 8 and generates the UIviews, reports, etc., described herein. In yet other embodiments, someof functionalities 420A-420J of analyte processor 490 may be executed byone or more of display devices 14-20 while the remaining functionalitiesmay be executed by a cloud-based server.

In some aspects, analyte processor 490 or a report generator therein maygenerate a view for display at a user interface and/or for display onone or more widgets at a user interface. The user interface view mayinclude one or more graphical representations comprising a plurality ofdifferent graphically distinct elements representative of processedanalyte data and/or other information.

In some aspects, system 100 may generate performance reports and/or userinterface views dynamically. For example, the analyte processor 490 mayreceive a request to generate a report or a user interface view. Inresponse to the request, the analyte processor 490 may then select thereports and/or interface views to provide. In certain embodiments, thisselection may be performed based on metadata. The metadata may includeinformation about the request, information about or representative ofthe user (e.g., user's personal information, user's analyte informationas provided by continuous analyte sensor system 8, etc.), the type ofdevice being used to display the report and/or measure the analyteconcentration level, rules, and the like.

The selection may be considered dynamic in the sense that the reportand/or user interface view selection varies for each request based onmetadata. The report or user interface view may then be generated toinclude at least one selected report and/or user interface view and thenprovided to a user interface for presentation.

In some aspects, the sensor electronics 12 may include electroniccircuitry associated with measuring and processing data generated by thecontinuous analyte sensor 10. This generated continuous analyte sensordata may also include algorithms, which can be used to process andcalibrate the continuous analyte sensor data, although these algorithmsmay be provided in other ways as well. The sensor electronics 12 mayinclude hardware, firmware, software, or a combination thereof toprovide measurement of levels of the analyte via a continuous analytesensor, such as a continuous glucose sensor. An example implementationof the sensor electronics 12 is described further below with respect toFIG. 2.

The sensor electronics 12 may, as noted, couple (e.g., wirelessly andthe like) with one or more devices, such as display devices 14, 16, 18,and/or 20. The display devices 14, 16, 18, and/or 20 may be configuredfor presenting (and/or alarming) information, such as sensor informationtransmitted by the sensor electronics 12 for display at the displaydevices 14, 16, 18, and/or 20.

The display devices may include a relatively small, key fob-like displaydevice 14, a relatively large, hand-held display device 16, a cellularphone display device 18 (e.g., a smart phone, a tablet, and the like), acomputer 20, and/or any other user equipment configured to at leastpresent information (e.g., a medicament delivery information, discreteself-monitoring glucose readings, heart rate monitor, caloric intakemonitor, and the like).

In some aspects, the relatively small, key fob-like display device 14may comprise a wrist watch, a belt, a necklace, a pendent, a piece ofjewelry, an adhesive patch, a pager, a key fob, a plastic card (e.g.,credit card), an identification (ID) card, and/or the like. This smalldisplay device 14 may include a relatively small display (e.g., smallerthan the large display device) and may be configured to display certaintypes of displayable sensor information, such as a numerical value andan arrow.

In some aspects, the relatively large, hand-held display device 16 maycomprise a hand-held receiver device, a palm-top computer, and/or thelike. This large display device 16 may include a relatively largerdisplay (e.g., larger than the small display device) and may beconfigured to display information, such as a graphical representation ofthe continuous sensor data including current and historic sensor dataoutput by continuous analyte sensor system 8.

In some aspects, the continuous analyte sensor 10 may comprise a sensorfor detecting and/or measuring analytes, and the continuous analytesensor 10 may be configured to continuously detect and/or measureanalytes as a non-invasive device, a subcutaneous device, a transdermaldevice, and/or an intravascular device. In some example aspects, thecontinuous analyte sensor 10 may analyze a plurality of intermittentblood samples, although other analytes may be used as well.

In some aspects, the continuous analyte sensor 10 may comprise a glucosesensor configured to measure glucose in the blood using one or moremeasurement techniques, such as enzymatic, chemical, physical,electrochemical, spectrophotometric, polarimetric, calorimetric,iontophoretic, radiometric, immunochemical, and the like. In aspects inwhich the continuous analyte sensor 10 includes a glucose sensor, theglucose sensor may be comprise any device capable of measuring theconcentration of glucose and may use a variety of techniques to measureglucose including invasive, minimally invasive, and non-invasive sensingtechniques (e.g., fluorescent monitoring), to provide a data, such as adata stream, indicative of the concentration of glucose in a user. Thedata stream may be raw data signal, which is converted into a calibratedand/or filtered data stream used to provide a value of glucose to a useror a caretaker (e.g., a parent, a relative, a guardian, a teacher, adoctor, a nurse, or any other individual that has an interest in thewellbeing of the user). Moreover, the continuous analyte sensor 10 maybe implanted as at least one of the following types of sensors: animplantable glucose sensor, a transcutaneous glucose sensor, implantedin a user vessel or extracorporeally, a subcutaneous sensor, arefillable subcutaneous sensor, an intravascular sensor.

Although the description herein refers to some examples that include acontinuous analyte sensor 10 comprising a glucose sensor, the continuousanalyte sensor 10 may comprise other types of analyte sensors, as well.Moreover, although some aspects may refer to the glucose sensor as animplantable glucose sensor, other types of devices capable of detectinga concentration of glucose and providing an output signal representativeof glucose concentration may be used, as well. Furthermore, although thedescription herein refers to glucose as the analyte being measured,processed, and the like, other analytes may be used as well including,for example, ketone bodies (e.g., acetone, acetoacetic acid and betahydroxybutyric acid, lactate, etc.), glucagon, Acetyl Co A,triglycerides, fatty acids, intermediaries in the citric acid cycle,choline, insulin, cortisol, testosterone, and the like.

FIG. 2 is a block diagram 200 conceptually illustrating a sensorelectronics module communicating with multiple sensors, in accordancewith certain aspects of the present disclosure. As shown in FIG. 2, asensor electronics module 212 may be in communication with multiplesensors, including a glucose sensor 220, an altimeter sensor 222, anaccelerometer sensor 224, a temperature sensor 226, and a locationmodule 269 (e.g., a global positioning system (GPS) processor or othersource of location information) in accordance with some example aspects.Although FIG. 2 illustrates the sensor electronics module 212 incommunication with specific sensors, other sensors and/or devices may beused. Other devices and/or sensors may include, for example, heart ratemonitors, blood pressure monitors, pulse oximeters, an impedance sensor,a dialysis machine, caloric intake devices, and medicament deliverydevices. Moreover, one or more of these sensors may provide data to ananalyte processing system 400 (referred to herein as “processing system400”) and/or analyte processor 490 described further below. In someaspects, a user may manually provide some of the data to processingsystem 400 and/or analyte processor 490. For example, a user may providecalorie consumption information via a user interface to processingsystem 400 and/or analyte processor 490.

In the example illustrated in FIG. 2, each of the sensors 220, 222, 224,and/or 226 may communicate sensor data wirelessly to the sensorelectronics module 212. In some examples, the sensor electronics module212 may include one or more of the sensors 220, 222, 224, and/or 226. Insome example, the sensors 220, 222, 224, and/or 226 may be combined inany other configuration, such as, for example, a combinedglucose/temperature sensor (e.g., or a multi-analyte sensor) used totransmit sensor data to the sensor electronics module 212 using commoncommunication circuitry. Depending on the example, fewer or additionalsensors may communicate with the sensor electronics module 212. In someexamples, one or more of the sensors 220, 222, 224, and/or 226 may bedirectly coupled to the sensor electronics module 212 (e.g., coupled viaone or more electrical communication wires).

The sensor electronics module 212 may generate and transmit a datapackage to a device, such as display device 250. Display device 250 maybe any electronic device configured to receive, store, retransmit,and/or display displayable sensor data. Display device 250 may be any ofthe display devices 14, 16, 18, and/or 20 illustrated in FIG. 1. Thesensor electronics module 212 may analyze the sensor data from themultiple sensors and determine which displayable sensor data is to betransmitted based on one characteristics of the user, the display device250, a user of the display device 250, and/or characteristics of thesensor data. Thus, the customized displayable sensor informationtransmitted to the display device 250 may be displayed on the displaydevice with minimal processing by the display device 250.

FIGS. 3A and 3B are perspective and side views, 300A and 300B,respectively, of a sensor system including a mounting unit 314 andsensor electronics 12 attached thereto. In an example, shown in itsfunctional position, the mounting unit 314 may be matingly engaged withthe sensor electronics 12. In some examples, the mounting unit 314, alsoreferred to as a housing or sensor pod, may include a base 334 adaptedfor fastening to a user's skin. The base 334 may be formed from avariety of hard or soft materials, and may comprise a low profile forminimizing protrusion of the device from the user during use. The base334 may be formed at least partially from a flexible material, which isbelieved to provide, in some aspects, numerous advantages over othertranscutaneous sensors, which, unfortunately, can suffer frommotion-related artifacts associated with the user's movement when theuser is using the device. The mounting unit 314 and/or sensorelectronics 12 may be located over the sensor insertion site to protectthe site and/or provide a minimal footprint (utilization of surface areaof the user's skin).

In some example aspects, a detachable connection between the mountingunit 314 and sensor electronics 12 may be provided. The detachableconnection may enable improved manufacturability. Namely, the relativelyinexpensive mounting unit 314 may be disposed of when replacing thesensor system after its usable life, while the relatively more expensivesensor electronics 12 may be reusable with multiple sensor systems. Insome example aspects, the sensor electronics 12 may be configured withsignal processing. For example, the sensor electronics 12 may beconfigured to filter, calibrate, and/or provided with other algorithmsuseful for calibration and/or display of sensor information.

In some example aspects, the contacts 338 may be mounted on or in asubassembly (hereinafter referred to as a contact subassembly 336)configured to fit within the base 334 of the mounting unit 314 and ahinge 348 that allows the contact subassembly 336 to pivot between afirst position (for insertion) and a second position (for use) relativeto the mounting unit 314. The hinge may provide pivoting, articulating,and/or hinging mechanisms, such as an adhesive hinge, a sliding joint,and the like. The action of the hinge may be implemented, in someaspects, without a fulcrum or a fixed point about which the articulationoccurs. In some example aspects, the contacts 338 may be formed from aconductive elastomeric material, such as a carbon black elastomer,through which the continuous analyte sensor 10 extends, although thecontacts may be formed in other ways as well.

In some example aspects, the mounting unit 314 may be provided with anadhesive pad 308, disposed on the mounting unit's back surface andincluding a releasable backing layer. Thus, removing the backing layerand pressing the base 334 of the mounting unit onto the user's skin mayallow for adherence of the mounting unit 314 to the user's skin.Additionally or alternatively, an adhesive pad may be placed over someor all of the sensor system after sensor insertion is complete to ensureadhesion, and optionally to ensure an airtight seal or watertight sealaround the wound exit-site (or sensor insertion site). Appropriateadhesive pads may be chosen and designed to stretch, elongate, conformto, and/or aerate the region (e.g., user's skin). The configurations andarrangements that provide water resistant, waterproof, and/orhermetically sealed properties may be provided with some of the mountingunit/sensor electronics aspects described herein.

FIG. 4 is a block diagram conceptually illustrating an example analyteprocessing system, in accordance with some example aspects of thepresent disclosure. As shown in FIG. 4, the processing system 400 mayinclude one or more user interfaces 410A-C, such as a browser, anapplication, a widget, and/or any other type of user interfaceconfigured to allow accessing and/or interacting with analyte processor490 via, for example, network 406 and a load balancer 412. The analyteprocessor 490 may further be coupled to repository 475. User interfaces410A-C may be associated with or provided as part of display devices 14,16, 18, 20 and/or any other display devices used by a user to view theuser interface views, reports, etc., described herein.

Processing system 400 may also receive data from source systems, such ashealth care management systems, patient management systems, prescriptionmanagement systems, electronic medical record systems, personal healthrecord systems, and the like. This source system information may providemetadata for dynamic report generation.

Processing system 400 may be implemented in a variety of configurationsincluding stand-alone, distributed, and/or cloud-based frameworks.Processing system 400 may be implemented in a cloud-based framework,such as a software-as-a-service (SaaS) arrangement in which the analyteprocessor 490 is hosted on computing hardware, such as servers and datarepositories maintained remotely from an entity's location (e.g., remotefrom a user, a health service provider, and like end-user) and accessedover network 406 by authorized users via a user interface, such as userinterface 410A, 410B, and/or 410C, and/or a data retriever 465.

In addition to the example illustrated in FIG. 4, processing system 400may be implemented as a SaaS-based system including a plurality ofservers, each of which may be virtualized to provide one or more analyteprocessors 490. Moreover, each of the virtualized analyte processors 490may serve a different tenant, such as an end-user, a clinic, a userwearing a sensor, and the like. To make more efficient use of computingresources of a SaaS provider and to provide important performanceredundancies and/or reliability, it may, in some aspects, advantageousto host multiple tenants (e.g., users, clinics, etc. at user interfaces410A-C and/or data retriever 465) on a single processing system 400 thatincludes multiple servers and that maintains data for all of themultiple tenants in a secure manner at repository 475 while alsoproviding customized solutions that are tailored to each tenant.

Referring again to FIG. 4, in some example aspects, processing system400 may provide a cloud-based diabetes data management frameworkconfigured to receive patient-related data from various devices. Variousdevices may include, and is not limited to, a medical device, a glucosemeter, a continuous glucose monitor, a continuous analyte sensor system8, display devices 14, 16, 18, and/or 20, source systems, a deviceproviding food consumption information (e.g., such as carbohydrates)associated with food consumed by a user, medicament delivery data, timeof day, temperature sensors, and/or exercise/activity sensors. In someexample aspects, the cloud-based diabetes data management may receivethe data programmatically with little (or no) intervention on the partof a user. The data received from devices, source systems, and the like,may be in a variety of formats and may be structured or unstructured.For example, in some example aspects, the processing system 400 mayreceive raw sensor data, which has been minimally processed or analyzed.The received data may then formatted, processed (e.g., analyzed), and/orstored in order to enable analyte data visualization.

For example, a data retriever 465 may be implemented at one or moredevices, such as computer 20 coupled to continuous analyte sensor system8. In this example, data retriever 465 may format sensor data into oneor more common formats compatible with analyte processor 490 and mayprovide the formatted data to analyte processor 490 such that analyteprocessor 490 may analyze the formatted data. Although FIG. 4 depicts asingle data retriever 465, in some example aspects, a plurality of dataretrievers 465 may be used to format data from a plurality of devicesand/or systems. In certain embodiments, data retriever 465 may executeon or be hosted at any other one of display devices, such as displaydevices 14, 16, and/or 18, in communication with continuous analytesensor system 8.

In some example aspects, the data retriever 465 may be accessed througha kiosk including a processor, such as a dedicated computer, configuredwith a user interface, or may be accessed via a secure web-basedinterface residing on a non-dedicated computer.

In some example aspects, the first time a processor (e.g., a computer, asmart phone, and any other device) accesses processing system 400, thedata retriever may be programmatically installed on the processor bydownloading software for the data retriever to the processor's memory.The downloaded software may then be programmatically installed on theprocessor, and then data retriever may generate a view which may bepresented on a user interface.

In some aspects, this user interface may allow a user to select an icon,such as a fetch icon, to programmatically start a data transfer toanalyte processor 490. For example, a user selects the fetch icon at theuser interface on a processor, such as computer 20, which initiates adata transfer from a continuous analyte sensor system 8 coupled to dataretriever 465 and analyte processor 490. In some aspects, the fetch iconmay be implemented as a software widget. Moreover, the software widgetmay be placed on a webpage, so that when selected a fetch process beginsfor a registered user.

Moreover, the software associated with the data retriever 465 mayinclude a self-updating mechanism, so that when a fetch is selected atthe user interface, the data retriever programmatically checks for anupdate (e.g., software, drivers, data, and the like) at analyteprocessor 490 (or another designated computer) and installs the update.The update may be performed programmatically with little (or no)intervention by a user. Data downloads from a device or system to thedata retriever 465 may be performed using a wired connection, such as adevice-specific download cable, or wirelessly, when the device and theprocessor are equipped for wireless data transfer.

The analyte processor 490 may check data downloaded by the dataretriever 465 for transmission-related errors, data formatting,device-related error codes, validity of the data, duplicate data points,and/or other aspects of the data. Moreover, if out-of-range data pointsor device errors are found, the analyte processor 490 may identify thosedata points. For example, the analyte processor 490 may flag those datapoints, correct the identified data points programmatically or by asystem administrator, and store the corrected data points. Moreover, theanalyte processor 490 may be configured by a user, such as a clinician,doctor, and the like, to perform additional data processing steps, suchas correcting time of day, correcting the date, and analyzing data byspecific cohorts, groups, and/or relationships (e.g., demographics, suchas age, city, state, gender, ethnicity, Type I diabetes, Type IIdiabetes, age of diabetes diagnosis, lab results, prescription drugsbeing used, self-reported conditions of the patient, diagnosedconditions of the patient, responses to questions posed to patient, andany other metadata representative of the user). Once the analyteprocessor 490 performs initial data processing (e.g., checks, cleaning,and analysis), the processed data and/or the raw data provided by thedata retriever may be stored at repository 475.

The processing at analyte processor 490 may also include associatingmetadata with the data received from the devices and/or sensors.Examples of metadata may include, but is not limited to, patientinformation, keys used to encrypt the data, patient accelerometer,location data (e.g., location of patient or location of patient'sclinic), time of day, date, type of device used to generate associatedsensor data. The patient information may include the patient's age,weight, sex, home address and/or any past health-related information,such as whether the patient has been diagnosed as a type 1 or type 2diabetic, high-blood pressure, or as having any other health condition.

The processing may also include analysis, such as determining one ormore descriptive measurements and/or generating one or more userinterface views based on received information, the metadata, anddescriptive measurements. These descriptive measurements may includestatistics (e.g., median, inner and outer quartile ranges, mean, sum, n,standard deviation, and coefficients of variation). Examples of userinterface views are depicted in FIGS. 7-14F and 19-55.

In the example of FIG. 4, user interfaces 410A-C may be used by one ormore entities, such as end-users, health care providers, clinics,patients, research groups, health systems, medical device manufacturersand the like. These entities may remotely access processing system 400via user interface 410A-C (e.g., of display devices 14, 16, 18, and/or20) to request an action, such as retrieve analyte data, provide analytedata, request analysis of analyte data, request generation of reportsincluding modules having views presenting descriptive measurements ofthe analyte data, present analyte data and reports, and the like. Otherexamples of actions include providing sensor data, such as glucose data,carbohydrate data, insulin pump data, and the like, to the analyteprocessor 490, initiating processing of the sensor data, initiatinganalysis of the sensor data, and storing data at repository 475. In someexample aspects, the computing resources provided by analyte processor490 may comprise one or more physical servers virtualized to provide theanalyte processing services disclosed herein.

The data retriever 465 may obtain (e.g., receive, retrieve, etc.) datafrom one or more sources and provide any obtained data in a formatcompatible for use within analyte processor 490. In some aspects, dataretriever 465 may be implemented in one or more of the source systemsand/or devices providing data to analyte processor 490. For example,data retriever 465 may be implemented in one or more devices, such ascontinuous analyte sensor system 8, continuous analyte sensor 10,display devices 14, 16, 18, and/or 20, medicament delivery pump 2,glucose meter 4, computers/processors coupled to those devices, and anyother device capable of providing data to analyte data processing system400. In these aspects, data retriever 465 may receive data from a userdevice and format the data in a format compatible with analyte processor490. The data retriever 465 may also be implemented on source systems,such as disease management systems, weight management systems,prescription management systems, electronic medical records systems,personal health record systems, and the like. In these aspects, dataretriever 465 may obtain data from the source system and format the datain a format compatible with analyte processor 490.

In some example aspects, data retriever 465 may, as noted, be downloadedand/or provided automatically to a device, a computer, a system, and thelike. For example, when a user on a computer first accesses analyte dataprocessing system 400, analyte data processing system 400 mayautomatically install and configure the data retriever 465 on the user'scomputer. Once the install is complete, the data retriever 465 may beginfetching data for processing system 400 and format, if needed, the datato allow processing of the fetched data by analyte processor 490. Tofurther illustrate by way of an example, the data retriever 465 may bedownloaded onto a device, such as computer 20. In this example, whencomputer 20 receives sensor data from sensor electronics module 12, adata retriever 465 may provide sensor data and/or metadata in a formatcompatible with analyte processor 490.

In some example aspects, the analyte processor 490 may process thereceived data by performing one or more of the following: associatemetadata with the data received from the devices, sensors, sourcesystem, and/or data retriever, determine one or more descriptivemeasurements, such as statistics (e.g., median, inner and outer quartileranges, mean, sum, n, and standard deviation), validating and verifyingthe integrity of the received data from the devices, sensors, sourcesystem, and/or data retriever, process received data based on metadata(e.g., to select certain patients, devices, conditions, diabetic type,and the like), and/or correlate received data from the devices, sensors,source system, and/or data retriever so that the data may be comparedand combined for processing and analyzing.

Moreover, the results of any processing performed by analyte processor490 may be used to generate views presenting descriptive measurementsand/or comparisons of the analyte data (e.g., user interface viewsdepicted in FIGS. 7-14F and 19-55). The descriptive measurements and/orcomparisons may be presented, for example, as graphs, bar graphs, staticcharts, charts, badges, tables, figures, maps, plots, and/or othervisualizations.

Furthermore, the outputs generated by processing system 400 may beprovided via one or more delivery mechanisms, such as report deliverymodule 420K. For example, the report delivery module 420K may provideoutputs generated by analyte data processing system 400 via email (e.g.,as illustrated in FIGS. 7-14F), secure email, print, text, presentationsfor display at a user interface (such as at user interface 410A-C hostedat a tablet, phone (e.g., as illustrated in FIG. 57), or otherprocessor), machine-to-machine communications (e.g., via third partyinterface 420J), and any other communication mechanism.

In some example aspects, the views may be customized dynamically for useby, for example, an entity, such as an end-user, a clinician, ahealthcare provider, or a device manufacturer. Furthermore, the viewsmay be customized based on the types and/or quantity of sensors andsystems providing data to processing system 400 and metadata or thetypes thereof available to processing system 400. This customization maybe performed by a user, by processing system 400 programmatically, or acombination of both.

Analyte processor 490 may include, in some example aspects, anauthenticator/authorizer 420A for authorizing access to analyteprocessor 490, a data parser 420B for parsing requests sent to analyteprocessor 490, a calculation engine 420H for receiving data from sensorsand processing the received data into counts for use with histograms,logic 420C, a data filter 420D, a data formatter 420E, a reportgenerator 420G, a pattern detector 4201, a report delivery module 420Kfor delivering views in a format for the destination, and a third partyaccess application programming interface to allow other systems anddevice to access and/or interact with analyte processor 490.

Analyte processor 490 may receive a request from a user interface, suchas user interface 410A-C, to perform an action (e.g., provide data,store data, analyze/process data, request a report, and the like).Before analyte processor 490 services the request, the analyte processor490 may process the request to determine whether the request isauthorized and authenticated. For example, authenticator and authorizer420A may determine whether the sender of the request is authorized byrequiring a user to provide a security credential (e.g., a useridentifier, a password, a stored security token, and/or a verificationidentifier provided by text message, phone, or email) at a userinterface presented on a computer. If authorized, authenticator andauthorizer 420A may authenticate the sender of the request to checkwhether a security credential associated with sender of the requestindicates that the sender (e.g., a user at user interface 410A) isindeed permitted to access a specific resource at analyte dataprocessing system 400 in order to perform the action, such as store (orupload) data at repository 475, perform analyze/process data, and/orrequest user interface view generation.

To further illustrate, the data retriever 465 associated with acontinuous analyte sensor system 8 and a computer 20 may be authorizedand authenticated by authenticator and authorizer 420A to access analyteprocessor 490 in order to write data to a buffer or other storagemechanism, such as repository 475. On the other hand, an entity, such asa user, at user interface 410A may be authorized and then authenticatedby authenticator and authorizer 420A to access analyte processor 490,but only permitted to access certain information. In this secondexample, the user at user interface 410A may be authorized andauthenticated to access repository 475 to view certain informationcorresponding to the user's own analyte data (e.g., glucose data) andaccess reports generated for the analyte data, but the user will not beauthorized and authenticated to access another user's data.

Once authorized and/or authenticated, the request received at analyteprocessor 490 may then be parsed by data parser 420B to separate anydata, such as sensor data, metadata, and the like, from the request. Insome aspects, data parser 420B may perform check data formatting,device-related error codes, validity of the data, duplicate data points,and/or other aspects of the data. Moreover, the data parser 420B mayassociate additional metadata with the separated data. The metadata mayinclude any of the metadata described herein, including an owner of thedata, a key to track the data, an encryption key unique to each user,time of day, date information, one or more locations where the data is(or will be stored), and the like. In some example aspects, the dataparsing 420 may provide data to the calculation engine 420H forformatting the data into counts and histograms as described furtherbelow.

In some example aspects, the request (or the parsed data therein) may beprocessed by calculation engine 420H. The calculation engine 420H maypreprocess the data received from devices, sensors, and the like to form“counts.” The counts may represent a measured value, such as an analytevalue measured by a sensor, a glucose value measured by a sensor, acontinuous glucose value measured by a sensor, and/or other diabetesrelated information, such as carbohydrates consumed, temperature,physical activity level, and the like, and how often that measured valueoccurred.

The calculation engine 420H may then use the count 508 to performadditional processing. The additional processing may include storing thecount in repository 475, which may include one or more databases tostore the counts. Moreover, the count may be stored with metadata, suchas time of day/date information. Furthermore, the count may beencrypted, as noted, before storage in repository 475.

The calculation engine 420H may also use the count to update one or morehistograms. For example, rather than keep track of and process a user'sanalyte levels over a certain period of time using raw sensor datavalues, the calculation engine 420H may convert the data values intocounts. The counts may be added to histograms, for a given user.

In some example aspects, the calculation engine 420H may generate aplurality of histograms for a given user for a plurality of given timeperiods.

In some example aspects, the calculation engine 420H may also updateother histograms representative of aggregate count information.

Although the description with respect to the calculation engine 420Hrefers to a histogram, the histogram, as used herein, refers to a datastructure that includes one or more values associated with one or moretime intervals. For example, the histogram may represent one or morevalues, such as frequency of occurrence, associated with binscorresponding to one or more time intervals. Moreover, this datastructure may be stored at a database, such that the data structure isreadily accessed with a read, such as in a row of a database (or, forexample, in a column if a column database is used).

In some example aspects, repository 475 stores the histograms includingcounts in a database. For example, repository 475 may store data for auser that covers a time frame, such as 1 day, 2 days 7 days, 14 days, 30days, and/or any other time frame. In this example, the days may besubdivided into epochs, each of which has a corresponding histogramstored in repository 475. Moreover, each histogram may be stored as arow (or column) in a database at repository 475 to facilitate fast dataaccess.

Logic 420C of FIG. 4 may also process requests to perform an action(e.g., store, retrieve, process, analyze, report data, etc.) at analyteprocessor 490. Logic 420C may also determine one or more descriptivemeasurements, such as statistics (e.g., a median, inner and outerquartile ranges, a mean, a sum, a standard deviation, and the like)based on counts, histograms, and/or received sensor data. The logic 420Cmay provide these descriptive measurements to the report generator 420Gto enable report generation (e.g., generation of a view for presentationat user interfaces 410A-C). For example, the mean may be determined bysumming the product of the count and the bin value and then dividingthat sum by the sum of the counts.

Pattern detector 4201 of FIG. 4 may perform pattern detection on data(e.g., sensor data representative of blood glucose data, analytes,insulin pump data, carbohydrate consumption data, and the like)processed by analyte processor 490 and stored at repository 475.Moreover, the pattern detector 4201 may detect patterns retrospectivelyfor a predetermined time period defined by processing system 400 and/ora user.

In some example aspects, the pattern detector 4201 may receive inputdata from the repository 475. The input data may include, for example,analyte concentration data, for example from a continuous analytesensor, other analyte data, such as rate of change, predictiveconcentrations etc. In some example aspects, input data may also includeother data such as temperature data, accelerometer data, insulin pumpdata, carbohydrate consumption data, food intake data, nutrition intakeor breakdown information, time of day, exercise and/or activity data,awake/sleep time intervals, medications information, or other similardata relating to activities of the user that may impact one or morebiological parameters of the user.

Moreover, the input data may comprise historical data obtained over atime frame, such as 8 hours, 1 day, 2 days, 7 days, 14 days, 30 days,and/or any other time frame. For example, the input data may include“counts” representative of monitored analyte detection levels (e.g.,glucose concentration levels) received and stored at processing system400 over a period covering a four-week (or more) time frame. Asmentioned above, “counts” may be stored in repository 475 with metadata,such as time of day/date information, to be used as input data at alater time. In another example, the input data may include histogramsupdated by “counts” of the user. The histogram may include an x-axis ofanalyte concentration values and a y-axis of the number of occurrencesfor each analyte concentration value. The histogram associated with agiven user/patient may be an example of input data used by patterndetector 4201.

The pattern detector 4201 may analyze the input data for patterns. Forexample, patterns may be recognized based on one or more predefinedrules (also referred to as criteria or triggers). Furthermore, the oneor more predefined rules may be variable and adjustable based userinput. For example, some types of patterns and rules defining patternsmay be selected, turned on and off, and/or modified by a user, a user'sphysician, or a user's guardian, although processing system 400 mayselect, adjust, and/or otherwise modify rules programmatically as well.In another example aspect, one or more patterns may be based onpredefined rules set by factory settings or device settings.

The pattern detector 4201 may detect the pattern and generate an output,which may be provided to report generator 420G. Moreover, the output mayinclude a retrospective analysis of the input data and any patternsdetermined by pattern detector 4201.

The data filter 420D may be used to check whether an output generated byanalyte processor 490, such as a response for certain types of data, areport, and the like, does not violate a data rule. For example, thedata filter 420D may include a data rule to check whether a responseincludes data, such as PII, to a destination which is not authorized orallowed to receive the response (e.g., based upon authorization andauthentication and the corresponding role of the user making therequest).

The data formatter 420E may format data for delivery based on the typeof destination. For example, the data formatter 420E may format a viewbased on whether it is being sent to a printer, a user interface, asecure email, another processor, and/or any other similar device orplatform.

The report generator 420G may generate one or more reports and/or userinterface views. The reports/views may provide descriptive information,such as statistical information, representative of the sensor datareceived at analyte processor 490. Moreover, the report/view may providea retrospective analysis of the sensor data stored at repository 475.For example, the report/view may provide statistical information basedon sensor data (and/or corresponding histograms including counts) over atime frame, such as 8 hours, 1 day, 2 days, 7 days, 14 days, 30 days,and/or any other time frame. Moreover, the report/view may allow a userto view the information and identify trends and other health relatedissues.

In some example aspects, report generator 420G generates reports and/orviews based on data received and/or stored at processing system 400(e.g., using sensor data, metadata, counts, histograms, informationassociated with a request to generate a report, and the like). Forexample, report generator 420G may select one or more features ormodules for providing as part of UIs displayed (FIGS. 5-57) to a userbased on a request received by a user to generate a report. In certainembodiments, the request may include information such as the identity ofthe patient, identity of the requesting device, a type of report beingrequested, and/or the like. The request may also specify a time framefor the report and/or as any other information required to authenticatethe device requesting device or user.

The report generator 420G may also select one or more features ormodules for providing as part of UIs displayed (FIGS. 7-14F and 19-55)to a user based on metadata including rules, templates, and/or the like.This metadata may describe one or more of the following: types of dataavailable; amount of data; types of devices being used; userpreferences; size of the user interface available to present report;patient demographics; patient information including report preferences,types and quantity of devices used, and display size being used topresent the report, and other data related to the user, devices, and thelike; rules, such as whether a module can be used with certain devices(for example, certain reports may only be suitable for continuous bloodglucose, rather than discrete measurements), whether a module can beused with certain patient conditions (for example, a caregiver mayestablish a rule requiring a certain report based on a patient'sdemographic, history, or condition), whether a module may be used oncertain display sizes, whether a module may be used given a certainvolume of data or device type; and/or one or more templates. Forexample, the selection of modules may be performed based on metadataincluding user preferences for certain modules, a type of device beingused, a display area of the device, and a rule defining which modulescan be used given the type of device, a patient state/condition, and thedisplay area of the device. Furthermore, the metadata may be stored at arepository, such as repository 475, although some of the metadata or maybe provided as part of the request received at 710.

A template may define the placement of one or more modules in a report.The framework defining the placement of each module may be a template(also referred to as a model or a wireframe). Moreover, templates may bedefined for certain devices or displays, so that when the request ismade and/or metadata obtained, the report generator 420G can dynamicallyselect, based on metadata, one or more modules into the predefinedtemplate. For example, a certain display device may be of a size whichallows four modules to be displayed in one way, while another displaydevice may be of a size which allows the four modules to be displayed ina different way, and so forth.

In some exemplary implementations, the metadata may include a pluralityof predefined templates configured for a specific patient, a specificcaregiver, a specific medical professional, a group of patients (e.g.,cohorts), a businessperson, and/or the like. As such, modules may bedynamically selected based on an evaluation of the metadata. Moreover,the use of the templates may, in some implementations, allow the dynamicgeneration of modules to be performed more rapidly, when compared to notusing the templates. In any case, when the report generator 420G selectswhich modules 710A-D are to be included in the report, the reportgenerator 420G may then obtain the underlying data (for example, sensordata, demographics, and the like) to be used in the selected modules.Examples of reports and/or user interface views that include features(also referred to as modules) are depicted in FIGS. 7-14F and 19-55.

According to certain aspects, logic 420C and pattern detector 4201 maybe used to determine one or more descriptive measurements, patterns, orrelationships for effective visualization. As described previously,logic 420C may determine a median, inner and outer quartile ranges, amean, a sum, a standard deviation, and other statistical measurementsbased on counts, histograms, and/or received sensor data. Patterndetector 4201 may analyze relationships among the data to determinepatterns. Relationships in the input data that may result in anidentified pattern may include, for example, an analyte level thatexceeds a target analyte range (e.g., which may be defined by a user, ahealth care provider, processing system 400, or a combination thereof),an analyte level that is below a target analyte range, a rapid change inanalyte levels from low to high (or vice versa), times of day when alow, a high, an at range, or rapid analyte level event occurs, days whena low, a high, an at range, and/or a rapid analyte level event occurs.

Additional examples of the types of relationships in the input data thatmay be considered a pattern include very high and/or very low analyteevents by time of day. As an example, in aspects where the analyte formeasurement may be glucose, a pattern may be identified in situationswhere the user has low analyte concentrations around the same time inthe day (e.g., a hypoglycemic event). Another type of pattern, which maybe identified, is a “rebound high” situation. For example, a reboundhigh may be defined as a situation where a user overcorrects ahypoglycemic event by overly increasing glucose intake, thereby goinginto a hyperglycemic event. These events may be detected based on one ormore predefined rules. Patterns that may be detected include ahyperglycemic pattern, a hypoglycemic pattern, patterns associated witha time of day or week, a weighted scoring for different patterns basedon frequency, a sequence, and a severity.

In some aspects, patterns may be based on a custom sensitivity of auser/patient, a transition from a very low to a very high pattern, anamount of time spent in a severe event, and a combination of analytechange and time information. Detected patterns may also be patterns ofhigh variability of analyte data. Further, a pattern may be based on acombination of previous pattern data and a currently detected situation,whereby the combined information generates a predictive alert.

FIG. 5 illustrates an example user interface view 500 associated withsensor data representative of analyte level(s), specifically glucoseconcentration level(s), in a user, in accordance with some exampleaspects of the present disclosure. Patterns and statistics identified bylogic 420C and pattern detector 4201 may be presented in a performancereport. As shown in FIG. 5, a weekly report may be provided to a user ofa diabetes management application to provide relevant insights into auser's retrospective glucose values, patterns, and trends over time.

In a first feature of the user interface view 500, a time in rangestacked bar graph, representing a percentage of time the user was in atarget glucose range, a very high or high glucose range, and a very lowor low glucose range over a specified period (e.g., any continuous sevenday period), may be provided. The target glucose range may be defined asa different range for daytime (e.g., 6:00 AM-10:00 PM in the exampleshown) and nighttime (e.g., 10:00 PM-6:00 AM in the example shown)hours. The user's percentage of time in range may also be compared tothe previous week's percentage of time in range. In some examples, thestacked bar graph may be presented using different colors todifferentiate the percentages of time the user was in a target glucoserange, a very high or high glucose range, and a very low or low glucoserange over a specified period. In some examples, the stacked bar graphmay be presented using different size blocks (stacked in the stacked bargraph) for each of the ranges. The varying sizes may correlate to theamount of time the user spent in each range. For example, the largestblock size in the stacked bar graph may represent the glucose range theuser spent the most amount of time in over a specified period of time,while the smallest block size in the stacked bar graph may represent theglucose range the user spent the least amount of time in over aspecified period of time.

In a second feature of the user interface view 500, average glucose andstandard deviations statistics (e.g., determined by Logic 420C) may bepresented to a user. The average glucose and standard deviation may becalculated based on a specified period (e.g., any continuous seven dayperiod).

In a third feature of the user interface view 500, a user's patterns ofdaytime lows/highs and nighttime lows/highs may be reported. A daytimeor nighttime low pattern may be identified in situations where the userhas a pattern of low glucose concentration levels around similar timeseach day in a specified period (e.g., any continuous seven day period).A daytime or nighttime high pattern may be identified in situationswhere the user has a pattern of high glucose concentration levels aroundsimilar times each day in a specified period (e.g., any continuous sevenday period).

In a fourth feature of the user interface view 500, a compilation of auser's time in range may be presented in a scatter plot with a line ofbest fit. The line of best fit expresses the relationship between thedata points and identifies a user's time in range trend over a period oftime (e.g., a twelve hour period, 12:00 AM-12:00 AM in the exampleshown) for a specified period (e.g., any continuous seven day period).Additionally, target glucose ranges, for both daytime and nighttimehours, may be provided in the graph. The target glucose ranges may bedefined as a different range for daytime and nighttime hours. Forexample, as shown in the fourth feature, the graph may identify adaytime target glucose range using a figure in the shape of a sun (e.g.,daytime range shown in the feature is 80-180 mg/dL) and may identify anighttime target glucose range using a figure in the shape of a moon(e.g., nighttime range shown in the feature is 90-200 mg/dL). The fourthfeature may also provide different color bar graphs to make thedistinction between a user's time in a high or very high glucose range,a user's time in a low or very low glucose range, and a user's time inglucose range over a twelve hour period for a specified period.

In a fifth feature of the user interface view 500, a hyperlink may beprovided to a user for access to more detailed continuous glucosemonitoring (CGM) reports on a website.

In some examples, features one through five may be presented in avertical format to the user such that feature one may be at the top ofthe page (e.g., email) and feature five may be at the bottom of thepage.

Aspects of the present disclosure provide improvements in datavisualization of analyte data, including identifying new patterns andrelationships for improved communication of data-driven insights andtrends to a user. For example, the present disclosure provides improvedmethods, such as operations 600 shown in FIG. 6, for generating improveduser interface views, shown in FIGS. 7-14F. The user interface views,shown in FIGS. 7-14F are improved in comparison to user interface viewillustrated in FIG. 5. For example, additional information is providedthat may be used by the user to regulate the user's behavior, such as tocontrol the user's blood glucose concentration levels. While the analytefor measurement and visualization by the devices and methods describedherein is glucose, other biological parameters and/or analytes may beconsidered, as well.

Example Analyte Date Processing and Weekly Report

FIG. 6 is a flow diagram illustrating example operations 600 forgenerating a user interface view associated with sensor datarepresentative of glucose concentration level(s) in a user, inaccordance with some example aspects of the present disclosure.Operations 600 may be performed by a processing system, such as theanalyte processor 490. In some examples, the operations 600 may be usedfor generating one or more of the reports illustrated in FIGS. 7-14F anddescribed in more detail below.

Operations 600 may begin, at 602, by accessing sensor data including aplurality of blood glucose readings associated with the user during aplurality of analysis time periods in a current week. Each blood glucosereading is indicative of a blood glucose concentration level of the userat a respective time. In some examples, the plurality of blood glucosereadings may be collected by a continuous glucose monitor (CGM) worn bythe user. In some examples, the plurality of blood glucose readings maybe stored in the repository 475 and accessed by the analyte processor490.

At 604, operations 600 may include determining an average blood glucoseconcentration level of the user for the current week. For example, thecalculation engine 420H at the analyte processor 490 may compute theaverage blood glucose concentration level based on the plurality ofblood glucose readings.

At 606, operations 600 may include generating a performance report. Theperformance report may include the average blood glucose concentrationlevel of the user for the current week and a comparison of the averageblood glucose concentration level of the user for the current week toaverage blood glucose concentration levels of the user for at least twoprevious weeks, a per-day average blood glucose concentration level ofthe user, per-day percentages of blood glucose concentration level ofthe user at one or more blood glucose concentration level ranges, or acombination thereof. For example, the performance report may be a weeklyperformance report (e.g., one of the reports shown in FIGS. 7-14F). Theperformance report may be generated by the report generator 420G of theanalyte processor 490. In some examples, the calculation engine 420H,pattern detector 4201, and/or logic 420C may determine (e.g., compute,process, and/or generate) the information included in the performancereport.

At 608, operations 600 may include generating a user interface view ofthe performance report.

At 610, operations 600 may include providing the user interface view ofthe performance report for display. In some examples, the user interfaceview may be provided via email to a user device for display at a userinterface. In some examples, the user interface view may provide fordisplay within an application running on a user device for display at auser interface.

FIG. 7 illustrates an example wireframe 700 of a user interface view ina vertical layout and FIG. 8A illustrates an example wireframe 800 a ofthe user interface view in a horizontal layout, in accordance with someexample aspects of the present disclosure. FIGS. 8B-8G illustrateenlarged views of features (e.g., modules) one through six shown in thevertical and horizontal wireframes, 700 and 800 a, respectively, and aredescribed in more detail further below.

FIG. 9 illustrates an example user interface view 900 associated withsensor data representative of glucose concentration level(s) in a userin a vertical layout which corresponds to the wireframe 700 of FIG. 7and FIG. 10A illustrates an example user interface view 1000 a in ahorizontal layout which corresponds to the wireframe 800 a of FIG. 8, inaccordance with some example aspects of the present disclosure. FIGS.10B-10G illustrate enlarged views of features one through six shown inthe vertical and horizontal user interface views, 900 and 1000 a,respectively, and are described in more detail further below.

A horizontal or vertical display of the user interface view may be basedon a type or configuration of a user device. For example, a verticallayout may be useful for a phone, tablet, or other smaller user device,while a horizontal layout may be useful for a desktop computer or alaptop computer. Although both horizontal and vertical orientations areshown, only one orientation may be displayed to the user. In someaspects, the orientation of the user interface view may be automaticallyselected and displayed based on a type of a user device.

As shown in the example user interface views of FIGS. 9-10G, andcorresponding wireframes of FIGS. 7-8G, a weekly report may be providedto a user of a diabetes management application to provide relevantinsights into a user's retrospective glucose values, patterns, and/ortrends over time.

The report may provide an average weekly glucose for one or more timeranges (e.g., weeks). The time range and the number of time ranges maybe configurable. The average weekly glucose may be computed bycalculation engine 420H and/or logic 420C of FIG. 4. As shown in FIGS.8B and 10B, in a first feature 800 b, 1000 b of wireframes 700, 800 aand user interface views 900, 1000 a, a visualization comparing anaverage blood glucose concentration level of a user for the current weekto average blood glucose concentration levels of the user for at leasttwo previous weeks (e.g., average blood glucose concertation level threeweeks ago, two weeks ago, and last week) may be provided.

As shown in FIGS. 8B and 10B, a first feature 800 b, 1000 b ofwireframes 700, 800 a and user interface views 900, 1000 a may furtherprovide a date associated with the time range (e.g., Sep. 6, 2020through Sep. 11, 2020) for the user's average glucose (e.g., 156 mg/dLin the example in FIG. 10B). To provide context to this glucose average,the user's average glucose may be compared to an average glucosecalculated for one or more previous weeks (e.g., “Last week”, “Two weeksago”, “Three weeks ago” in the example in FIG. 10B). This comparisonvisualization may provide insight on the user's blood sugar consistency,improvement, and/or poor performance over multiple weeks.

As shown in FIGS. 8D and 10D, a third feature 800 d, 1000 d ofwireframes 700, 800 a and user interface views 900, 1000 a may provide avisualization comparing the per-day average blood glucose concentrationlevels of a host and the per-day percentages of blood glucoseconcentration levels of a host at one or more blood glucoseconcentration level ranges. The per-day average blood glucoseconcentration levels may be computed by calculation engine 420H and/orlogic 420C of FIG. 4 based on continuously monitored blood glucose data.The report may also provide per-day percentages of blood glucoseconcentration levels of a user at one or more blood glucoseconcentration level ranges. For example, pattern detector 4201,calculation engine 420H, and/or logic 420C of FIG. 4 may compare bloodglucose concentration level data throughout the course of a 24 hourperiod to a predetermined target or “normal” blood glucose concentrationlevel value or range to identify periods in which the user's bloodglucose concentration level is “in range” or “out of range”. The analyteprocessor can then determine a percentage of time during a 24 hourperiod that the user's blood glucose concentration level was “in range”(referred to herein as “time in range” or “TIR”).

As shown in FIGS. 8D and 10D, a day-by-day bar graph may be presented toillustrate the user's blood glucose concentration levels. A day-by-daybreakdown may allow the user to understand their problem days throughoutthe week and adjust their lifestyle, diet, or other associated factorsaccordingly. The visualization may also provide a percentage of time inrange per day to provide additional context to the user's average dailyglucose concentration levels. For example, as shown in FIG. 10D, onSaturday the host may experience their highest average glucose of theweek at 234 mg/dL. With a corresponding 66% time in the target glucoserange, the user may be able to conclude that a majority of the timeoutside of their target range was spent in a high or very high glucoserange (instead of a low or very low glucose range).

As shown in FIGS. 8G and 10G, a sixth feature 800 g, 1000 g ofwireframes 700, 800 a and user interface views 900, 1000 a may be abutton provided to re-direct the user to a website or an application(app) on the user's device that provides the user's and/or host's moredetailed continuous glucose monitoring (CGM) reports. Whether the buttonre-directs the user to a website or redirects the user to an app maydepend on the type or configuration of the user device being used todisplay the button. Unlike the hyperlink of feature five in FIG. 5, thebutton may be larger than the hyperlink thereby capturing the user'sattention and encouraging greater interaction between the user and theuser interface.

A second feature 800 c, 1000 c shown in FIGS. 8C and 10C, a fourthfeature 800 e, 1000 e shown in FIGS. 8E and 10E, and a fifth feature 800f, 1000 f shown in FIGS. 8F and 10F of wireframes 700, 800 a and userinterface views 900, 1000 a, corresponding to the time in range stackedbar graph, the patterns summary, and the trends summary, may be similarto features one, three, and four depicted in FIG. 5.

In some examples, features one through six of wireframes 700, 800 a anduser interface views 900, 1000 a may be presented chronologically: fromtop to bottom, as shown in the vertical layouts of FIGS. 7 and 9, orfrom left to right, as shown in the horizontal layouts in FIGS. 8A and10A. In some examples, feature one may be stacked on top of feature twoin the horizontal layout. In some examples, feature four may be stackedon top of feature five in the horizontal layout. In some example,features one through six may be presented in any random order.

FIG. 11A illustrates an example wireframe 1100 a of another example userinterface view in a vertical layout, and FIG. 11B illustrates an examplewireframe 1100 b of the user interface view in a horizontal layout, inaccordance with some example aspects of the present disclosure. FIGS.11C-11F illustrate enlarged views of features one through four shown inthe vertical and horizontal wireframes, 1100 a and 1100 b, respectively,and are described in more detail further below.

FIG. 12A illustrates an example user interface view 1200 a associatedwith sensor data representative of glucose concentration level(s) in auser in a vertical layout which corresponds to the wireframe 1100 a andFIG. 12B illustrates an example user interface view 1200 b in ahorizontal layout which corresponds to the wireframe 1100 b, inaccordance with some example aspects of the present disclosure. FIGS.12C-12G illustrate enlarged views of features one through four shown inthe vertical and horizontal user interface views, 1200 a and 1200 b, andare described in more detail further below.

Although both horizontal and vertical orientations are shown, only oneorientation may be displayed to the user. In some aspects, theorientation of the user interface view may be automatically selected anddisplayed based on a type of a user device.

As shown in the example user interface views of FIGS. 12A and 12B, andcorresponding wireframes of FIGS. 11A and 11B, a weekly report may befeature based, beginning with glucose and adding other interchangeableelements, to provide relevant insights into a user's retrospectiveglucose values, patterns, and trends over time with respect to otherfactors such as insulin, meals, and/or other factors which may affectthe user's glucose levels. In some examples, a user may be able toswitch the presented categories on the user interface by interactingwith a button provided on the user interface view (e.g., “Switch it up!”button provided in FIG. 12G).

As shown in FIGS. 11C and 12C, in a first feature 1100 c, 1200 c ofwireframes 1100 a, 1100 b and user interface views 1200 a, 1200 b,information related to the user's glucose levels over a specified timeperiod may be provided. The glucose information may include an averageglucose (e.g., determined by Logic 420C of FIG. 4) for the current weekin comparison to a previous week's average glucose and the month'saverage glucose, a time in range stacked bar graph broken down intoblocks (stacked on top of each other) which each correlate to the user'stime spent in a target glucose range, a very high glucose range, a highglucose range, a low glucose range, and a very low glucose range, asummary of the number of hours a user has spent in hyper (e.g.,hyperglycemia, when the user's blood sugar is too high) and hypo (e.g.,hypoglycemia, when the user's blood sugar is too low) during a specifiedperiod (e.g., a week), a day-by-day bar graph of the user's averageglucose levels in comparison to a weekly average, and a buttonre-directing the user at the user interface to be re-directed to awebsite housing detailed CGM reports.

As shown in FIGS. 11D and 12D, in a second feature 1100 d, 1200 d ofwireframes 1100 a, 1100 b and user interface views 1200 a, 1200 b,information related to the host's insulin levels over a specified timemay be provided. The insulin information may include an average numberof insulin units taken by the user. For example, the average number ofinsulin units for the current week may be compared to a previous week'saverage insulin units and the month's average insulin units. Further, astacked bar graph illustrating the user's basal insulin intake to bolusinsulin intake and a user's average daily basal and bolus units may beprovided in the second feature. As used herein, basal is an hourly dripof insulin which replaces long lasting insulin, and bolus is an extradose that is given for the food eaten and to correct the blood sugarwhere a user's blood sugar is high. The analyte processor 490 maydetermine average insulin amounts based on information input by theuser, information automatically uploaded and collected, etc.

As shown in FIGS. 11E and 12E, in a third feature 1100 e, 1200 e ofwireframes 1100 a, 1100 b and user interface views 1200 a, 1200 b,information related to the user's carb intake over a specified time maybe provided. The carb intake information (i.e., calculated based on auser's input of events, including meals logged by the user) may includean average number of carbs (i.e., determined by logic 420C of FIG. 4)for the current week in comparison to a previous week's average numberof carbs and the month's average number of carbs, a day-by-day bar graphof the user's average carb intake, and a day-by-day breakdown of theuser's percentage of time in a target glucose range. Percentage of timein the target glucose range may be provided in the third feature toassist the user in identifying a correlation, if any, between carbintake and its effect on the user's glucose level.

In some examples, the third feature of wireframes 1100 a, 1100 b anduser interface views 1200 a, 1200 b may also include information relatedto the user's activity over a specified time (e.g., additional thirdfeature 1200 f shown in FIG. 12F). The activity information (i.e.,calculated based on a user's input or tracker of events, including stepstaken by the user) may include an average number of steps (i.e.,determined by logic 420C of FIG. 4) for the current week in comparisonto a previous week's average number of steps and the month's averagenumber of steps, a day-by-day bar graph of the host's average stepstaken, and a day-by-day breakdown of the host's percentage of time in atarget glucose range. Percentage of time in the target glucose range maybe provided in the third aspect to assist the user in identifyingcorrelation, if any, between steps taken and its effect on the user'sglucose level.

As shown in FIGS. 11F and 12G, in a fourth feature 1100 f, 1200 g ofwireframes 1100 a, 1100 b and user interface views 1200 a, 1200 b,information related to the user's “best day” over a specified time maybe provided. Logic 420C may determine a user's “best day” based on theday the user spent the most time in a target glucose range. In someexamples, the user's glucose, insulin, carbs, and steps may be given tobetter understand the factors that contributed to and resulted in theuser's “best day”. A summary of contributing factors may also be givento motivate the user to experience more days similar to the user's “bestday”. A user may also receive trophies (also referred to herein asbadges) to reward the user for their “best day” behavior. In someexamples, the trophy may include a “Consistent Carbs Trophy” used toreward the user for having a day without any carb spikes. In someexamples, the trophy may include a “Bas/Bol Balancer Trophy” used toreward the user for keeping their basal/bolus ratio at a target ratio(e.g., 60/40), or better. In some examples, the trophy may include a“Time in Ranger Trophy” used to reward the user for spending over 80% oftheir week in a target glucose range. In some examples, a trophy theuser almost earned may be identified.

In some examples, features one through four of wireframes 1100 a, 1100 band user interface views 1200 a, 1200 b may be presentedchronologically; from top to bottom, as shown in the vertical layouts ofFIGS. 11A and 12A, or from left to right as shown in the horizontallayouts in FIGS. 11B and 12B. In some example, features one through fourmay be presented in any random order.

FIG. 13A illustrates an example wireframe 1300 a of another example userinterface view in a vertical horizontal layout, and FIG. 13B illustratesan example wireframe 1300 b of the user interface view in a horizontallayout, in accordance with some example aspects of the presentdisclosure. FIGS. 13C-13E illustrate enlarged views of features onethrough three shown in the vertical and horizontal wireframes, 1300 aand 1300 b, respectively, and are described in more detail furtherbelow.

FIG. 14A illustrates an example user interface view 1400 a associatedwith sensor data representative of glucose concentration level(s) in auser in a vertical layout which corresponds to the wireframe 1300 a, andFIG. 14B illustrates an example user interface view 1400 b in ahorizontal layout which corresponds to the wireframe 1300 b, inaccordance with some example aspects of the present disclosure. FIGS.14C-14F illustrate enlarged views of features one through four shown inthe vertical and horizontal user interface views, 1400 a and 1400 b,respectively, and are described in more detail further below.

Wireframe and user interface view, 1300 a and 1400 a, show verticalorientations of a wireframe and a user interface view, respectively,while wireframe and user interface view, 1300 b and 1400 b, showhorizontal orientations of a wireframe and user interface view,respectively. Although both horizontal and vertical orientations areshown, only one orientation may be displayed to the user. In someaspects, the orientation of the user interface view may be automaticallyselected and displayed based on a type of a user device.

As shown in the example user interface views of FIGS. 14A and 14B, andcorresponding wireframes of FIGS. 13A and 13B, a weekly report may beday-type based, which refers to a report that compares an “average day”to a “trouble day” and to a “best day”. A user's “best day” may bedetermined (e.g., determined by logic 420C) based on the day the userspent the most time in a target glucose range. Alternatively, a user's“trouble day” may be determined based on the day the user spent theleast amount of time in a target glucose range.

As shown in FIGS. 13C and 14C, in a first feature 1300 c, 1400 c ofwireframes 1300 a, 1300 b and user interface views 1400 a, 1400 b, atime in range stacked bar graph may be provided that represents apercentage of time the user was in a target glucose range, a very highor high glucose range, and a very low or low glucose range over aspecified period (e.g., any continuous seven day period). In someexamples, the target glucose range may be defined as a different rangefor daytime (e.g., 6:00 AM-10:00 PM) and nighttime (e.g., 10:00 PM-6:00AM) hours. The user's percentage of time in range may also be comparedto the previous week's percentage of time in range. In some examples,the stacked bar graph may be presented using different colors todifferentiate the percentages of time the host was in a target glucoserange, a very high or high glucose range, and a very low or low glucoserange over a specified period. In some examples, the stacked bar graphmay be presented using different size blocks (stacked in the stacked bargraph) for each of the ranges. The varying sizes may correlate to theamount of time the user spent in each range. For example, the largestblock size in the stacked bar graph may represent the glucose range theuser spent the most amount of time in over a specified period of time,while the smallest block size in the stacked bar graph may represent theglucose range the user spent the least amount of time in over aspecified period of time.

Additionally, the first feature of the user interface view may provide asummary overview of the user's average glucose, average insulin units,average carb intake, and average steps logged for a specified period(e.g., any continuous seven day period). The first feature may alsoprovide a trend and insight graph. The trend graph may include acompilation of a user's time in range presented in a scatter plot with aline of best fit. The line of best fit expresses the relationshipbetween the data points and identifies a user's time in range trend overa twelve hour period (e.g., 12:00 AM-12:00 AM in the example shown) fora specified period (e.g., any continuous seven day period).Additionally, target glucose ranges, for both daytime and nighttimehours, may be provided in the graph. The target glucose ranges may bedefined as a different ranges for daytime and nighttime hours. Forexample, the graph may identify a daytime target glucose range using afigure in the shape of a sun on the side of the trend graph and mayidentify a nighttime target glucose range using a figure in the shape ofa moon on the side of the trend graph (sun and moon figures not shown).The trend graph may also provide different color bar graphs to make thedistinction between a user's time in a high or very high glucose range,a user's time in a low or very low glucose range, and a user's time inglucose range over a twelve hour period for a specified period. Thetrend graph may also be a bar graph broken down into categories ofaverage insulin units, average carb intake, and average steps loggedover a particular period of time (e.g., twelve hours) for a specifiedperiod (e.g., any continuous seven day period). Insights may includesummaries of high and low periods and recommendations for mitigatingthese unwanted spikes.

As shown in FIGS. 13D and 14D, in a second feature 1300 d, 1400 d ofwireframes 1300 a, 1300 b and user interface views 1300 a, 1300 b,information related to the user's “trouble day” over a specified timemay be provided. In some examples, the user's glucose, insulin, carbs,and steps may be given to better understand the contributing factorsthat resulted in the user's “trouble day”. Additionally, trend graphsfor the user's “trouble day” may be provided. The trend graph maypresent a compilation of a user's glucose levels, insulin units, carbintake, steps logged, and hours of sleep over a particular period oftime (e.g., 24-hour period, such as 12:00 AM to 12:00 AM). The trendgraph may identify relationships the user may not have otherwise know.For example, as shown in the example Sunday trend graph, between about 6AM and about 9 AM the user experienced a first drop in their glucoselevel, and between about 3 PM to about 6 PM the user experienced asecond drop in their glucose level. During each of these time periods,the user logged multiple carbs. Although not the only factorcontributing to the user's glucose levels, there may exist an inverserelationship between glucose level and carb intake presented in thetrend graph, which the user may not have known previously. Accordingly,the user may take appropriate action to prevent future glucose daytimelows.

As shown in FIGS. 13E and 14E, in a third feature 1300 e, 1400 e ofwireframes 1300 a, 1300 b and corresponding user interface views 1400 a,1400 b similar information as the first feature may be provided;however, the data may be tailored to represent the user's “best day”.

As shown in FIG. 14F, in a fourth feature 1400 f of user interface views1300 a, 1300 b, a user may receive trophies (also referred to herein asbadges) to reward the user for their weekly behavior. In some examples,the trophy may include a “Step Master Trophy” used to reward the userfor having a day during the week where the user walked at least 10,000steps. In some examples, the trophy may include a “Time in Range Trophy”used to reward the user for spending over 80% of their week in a targetglucose range. In some examples, a trophy the user almost earned may beidentified. A button (e.g., “My Badge Collection” button) may also beprovided for a user to launch a view of all trophies (also referred toherein as badges) earned.

In some examples, features one through three of wireframes 1300 a, 1300b and features one through four of user interface views 1400 a, 1400 bmay be presented chronologically; from top to bottom, as shown in thevertical layouts of FIGS. 13A and 14A, or from left to right, as shownin the horizontal layouts in FIGS. 13B and 14B.

While the foregoing is directed to visualization of a user's analytedata (and in some cases, non-analyte data, such as carb intake or stepstaken) in specific formats for presentation, other formats may becustomized and/or designed to best meet the needs of each individualuser.

Example Analyte Data Processing and Visualization of Analyte Data onWidgets

Aspects of the present disclosure provide techniques for providing oneor more user interface views for display on one or more widgets. Morespecifically, aspects of the present disclosure provide techniques forvisualization of analyte data (and/or non-analyte data) on widgets. Insome aspects, with continuous glucose monitoring, information associatedwith a user's blood glucose data can be processed, presented to theuser, and updated in real-time or periodically via widgets. While theanalyte for measurement and visualization on widgets by the devices andmethods described herein is glucose, other biological parameters and/oranalytes may be considered, as well.

A hallmark of modern graphical user interfaces is that they allow alarge number of items to be displayed on a screen at the same time.Widgets are a user's portal to a variety of information and quickfunctionality. Widgets may communicate with a remote server to provideinformation to the user (e.g., a weather report), or widgets may providecommonly needed functionality (for example, a calculator), or widgetsmay act as an information repository (e.g., a notepad or calendar). Somewidgets may provide a combination of these functionalities. Some widgetsmay interact with remote sources of information, such as servers, toprovide information. For example, a weather feature may retrieve liveweather data from a remote server. Widgets may be interactive, so that auser may perform common input operations (such as clicking a mouse ortyping on a keyboard) to utilize the functionality of a widget.Additionally, widgets may act as a link to a program, such as, anapplication (app) running on a user's device. For example, by engagingwith a widget (e.g., clicking on a widget), a user may be re-directed toan app thereby allowing the user to enjoy all features, services, and/orinformation provided by the app.

Widgets may be “pinned” to a user interface in various spots and sizes,allowing for many different layouts. Widgets may be located in a viewthat is accessible with a swipe (e.g., from left to right) on a userdevice's home screen or may be accessible on the user device's homescreen (without a need to swipe). In some example aspects, widgets maynot overlap one another; if the user attempts to move one widget to theposition occupied by another widget, one of the widgets mayautomatically move out of the way to make room. In some example aspects,the position, configuration, and size of widgets may be saved when theuser interface is dismissed, so that the same state may be restored thenext time the dashboard is invoked.

FIG. 15 is a block diagram conceptually illustrating a softwarearchitecture 1500 for implementing widget functionality, in accordancewith some example aspects of the present disclosure. As shown in FIG.15, software architecture 1500 may include an operating system running adashboard server 1501, dashboard client(s) 1502, and widget(s) 1503.Dashboard configuration information 1504 may be used by server 1501and/or clients 1502 to specify the configuration options (e.g., accesslevels, etc.) for displaying widgets 1503. Clients 1502 may displaywidgets 1503 by rendering web pages into a view, the size of each viewmay be defined as metadata associated with the corresponding widget1503. Server 1501 may provide data for rendering a user interface layerthat may be overlaid on the desktop, or home screen, of the userinterface. Widgets 1503 may be rendered into the separate layer, andthen that separate user interface layer may be drawn on top of thenormal desktop or home screen, so as to partially or completely obscurethe desktop or home screen while the dashboard is active. In someaspects, widgets associated with a user's analyte data may be provided.

According to certain aspects, a widget may be provided by a user device(e.g., by an application running on the user device). In this,information displayed on the widget may be provided by a user device(e.g., rather than provided by a server). For example, some widgets mayoperate without a network connection. In some cases, information may beprovided by another user device having a connection with the userdevice, such as a wearable.

FIG. 16 illustrates an example dashboard 1600 (also referred to as“unified interest layers”) including a number of user interfaceelements, also referred to herein as “widgets”, in accordance with someexample aspects of the present disclosure. These user interfaceelements, or widgets, generally include software accessories forperforming useful, commonly needed functions. Examples of widgets mayinclude, without limitation, a calendar, a calculator, an address book,a package tracker, a weather feature, a health tracker, and the like.

Users may interact with and/or configure widgets as desired. Forexample, users may move widgets around the screen and/or resize widgets,if applicable. Some widgets may be resizable, and some may be of a fixedsize; the widget author may specify whether a widget may be resized.Some widgets may automatically resize themselves based on the amount ornature of the data being displayed.

FIG. 17 is a flow diagram illustrating example operations 1700 forgenerating a user interface view associated with sensor datarepresentative of a glucose concentration level in a user, in accordancewith some example aspects of the present disclosure.

Operations 1700 begin, at 1702, by accessing first data associated withblood glucose concentration level(s) of a user during a first timeperiod. For example, analyte processor 490 in FIG. 4 may access datastored in repository 475 and/or receive data from one or more sources.

At 1704, operations 1700 include analyzing the first data to generate afirst one or more user interface views associated with the first datafor display on one or more widgets. For example, one or more componentsof analyte processor 490 in FIG. 4 may process analyte data and/or otherinformation associated with a user to generate information to beprovided to the user in one or more widgets.

At 1706, operations 1700 include providing the first one or more userinterface views for display on the one or more widgets. For example,analyte processor 490 may provide the one or more widgets for display atthe UIs 410, the computer 20, and/or other user interfaces.

At 1708, operations 1700 include automatically updating the first one ormore user interface views for display on the one or more widgets. Forexample, the automatically updating includes accessing second dataassociated with blood glucose concentration levels of the user during asecond time period, analyzing the second data to generate a second oneor more user interface views associated with the second data for displayon the one or more widgets, and providing the second one or more userinterface views for display on the one or more widgets.

FIGS. 18A-18C provide a table 1800A-1800C categorizing example analytedata widgets, in accordance with some example aspects of the presentdisclosure. As shown in FIGS. 18A-18C, widgets may be categorized aseither a summary type widget, a motivation type widget, an event typewidget, or another type widget, in accordance with some example aspectsof the present disclosure.

A summary widget may be a widget that provides a short, cleardescription of the main facts and/or ideas about the user's collecteddata over a specified period of time (e.g., eight hours, three days,seven days, fourteen days, thirty days, or another defined time period).For example, a summary widget may provide a user's average glucose frommeasured sensor data over a period of seven days. A motivation typewidget may be a widget that provides a comparison of data and/or amessage used to incentivize a user to take action (e.g., maintainglucose stability, make a lifestyle change, etc.). For example, amotivation type widget may provide the user with an indication of their“best day” for the past seven days with a summary of factors whichcontributed to their “best day”. Such a widget may be used to motivatethe user to achieve similar results for other days (e.g., incentivizethe user to keep their average glucose for the next few days at the sameaverage glucose level as their “best day”). An event type widget may bea widget which tracks a user's health based on events the user haslogged. For example, a logged event may include a logged meal consumed(e.g., input of calories, carbs, etc.) or a logged period of time whenthe user engaged in some form of exercise (e.g., input of exercise type,calories burned, etc.). Accordingly, the event type widget may indicatea user's glucose level at the start of an event, one hour after theevent, and/or two hours after the event. Other types of widgets mayencompass widgets which do not fit into the other three categories. Forexample, another type widget may be a widget which displays a trendgraph of a user's average glucose level for a 24 hour period over thepast 14 days.

FIG. 19 illustrates an example summary widget 1900, in accordance withsome example aspects of the present disclosure. As shown in FIG. 19, thesummary widget 1900 may provide a summary of a user's percentage of timespent in a target glucose range and the user's average glucose over aseven day period.

FIG. 20 illustrates another example summary widget 2000, in accordancewith some example aspects of the present disclosure. As shown in FIG.20, the summary widget 2000 may provide a summary of a user's averageglucose, the user's standard deviation, and the user's glucosemanagement indicator (GMI) percentage over a fourteen day period.

FIG. 21 illustrates another example summary widget 2100, in accordancewith some example aspects of the present disclosure. As shown in FIG.21, the summary widget 2100 may provide a summary of a host's percentageof time spent in a target glucose range, the host's average glucose, andthe host's standard deviation over a defined time period, such as afourteen day period.

FIG. 22 illustrates another example summary widget 2200, in accordancewith some example aspects of the present disclosure. As shown in FIG.22, the summary widget 2200 may provide a summary of a host's percentageof time spent in a target glucose range, the host's average glucose, andthe host's GMI percentage over a defined time period, such as a fourteenday period.

FIG. 23 illustrates another example summary widget 2300, in accordancewith some example aspects of the present disclosure. As shown in FIG.23, the summary widget 2300 may provide a summary of a user's percentageof time spent in a target glucose range, very high and high glucoseranges, and very low and low glucose ranges over a defined time period,such as a fourteen day period. As shown, the summary widget 2300 mayprovide both numerical values and a stacked bar graph representing thepercentages. The stacked bar graph may be associated with color codingrepresenting severity of the ranges.

FIG. 24 illustrates another example summary widget 2400, in accordancewith some example aspects of the present disclosure. As shown in FIG.24, the summary widget 2400 may provide the information of the summarywidget 2300 in FIG. 23, a summary of a user's percentage of time spentin a target glucose range, very high and high glucose ranges, and verylow and low glucose ranges over a defined time period, such as afourteen day period, and further provide an indication of the positiveor negative percentage of change compared to a prior similar definedtime period, such as a prior fourteen day period.

FIGS. 25A and 25B illustrate another example summary type widget 2500 b,and its corresponding wireframe 2500 a, in accordance with some exampleaspects of the present disclosure. As shown in FIGS. 25A and 25B, thesummary type widget 2500 b may provide the information in the summarywidget 2300 in FIG. 23, a summary of a user's percentage of time spentin a target glucose range, very high and high glucose ranges, and verylow and low glucose ranges over a defined time period, such as afourteen day period, and further provide the host's average glucose, andthe host's GMI percentage over a same or different defined time period.

FIGS. 26A and 26B illustrate another example summary type widget 2600 b,and its corresponding wireframe 2600 a, in accordance with some exampleaspects of the present disclosure. As shown in FIGS. 26A and 26B, thesummary widget 2600 b may provide the information of the summary widget2500 b in FIGS. 25A and 25B, a summary of a user's percentage of timespent in a target glucose range, very high and high glucose ranges, andvery low and low glucose ranges over a defined time period, such as athirty day period, plus an indication of the positive or negativepercentage of change compared to a prior similar defined time period,such as a prior thirty day period, the user's average glucose, theuser's GMI percentage, and may further provide an indication of apattern (e.g., user's daytime high or nighttime low glucose patterns)over the defined time period. As shown, the indication may be providedas a narrative text.

FIG. 27 illustrates another example summary widget 2700, in accordancewith some example aspects of the present disclosure. As shown in FIG.27, the summary widget 2700 may provide a summary of a user's percentageof time spent in a target glucose range comparing percentages for thecurrent week, last week, this month, and year to date (YTD).

FIG. 28 illustrates another example summary widget 2800, in accordancewith some example aspects of the present disclosure. As shown in FIG.28, the summary widget 2800 may provide a summary of a user's percentageof time spent in a target glucose range comparing the last seven days,the last fourteen days, and the last thirty days plus an indication ofthe user's average glucose and standard deviation over the last sevendays, the last fourteen days, and the last thirty days. As shown, thepercentage of time in range may be provided as a stacked bar graph.

FIG. 29 illustrates another example summary widget 2900, in accordancewith some example aspects of the present disclosure. As shown in FIG.29, a summary widget 2900 may provide a summary of a user's percentageof time spent in a target glucose range, very high and high glucoseranges, and very low and low glucose ranges and an indication of thehours/minutes the user spent in each of these ranges over a defined timeperiod, such as a seven day period. As shown, the percentage of timespent in range may be represented by a stacked bar graph and the stackedbar graph may be associated with values for the number of hours spent invarious ranges associated with the stacked bar graph.

FIG. 30 illustrates another example summary widget 3000, in accordancewith some example aspects of the present disclosure. As shown in FIG.30, the summary widget 3000 may provide a summary of the hours/minutes auser spent in a target glucose range, a high glucose range, and a lowglucose range over a defined time period, such as a seven day period.

FIG. 31 illustrates another example summary widget 3100, in accordancewith some example aspects of the present disclosure. As shown in FIG.31, the summary widget 3100 may provide a summary of a number of highand low events a host participated in over a defined time period, suchas a seven day period.

FIG. 32 illustrates another example summary widget 3200, in accordancewith some example aspects of the present disclosure. As shown in FIG.32, the summary widget 3200 may provide a summary of a user's time inrange, which may presented in a graph with a line of best fit indicatingthe user's time in range trend over a defined time period, such as atwelve hour period (e.g., 12:00 AM-12:00 AM), for a particular duration,such as three days. An indication of the data included in the summarygraph may also be included in the summary widget 3200.

FIGS. 33A and 33B illustrate an example motivation type widget 3300 b,and its corresponding wireframe 3300 a, in accordance with some exampleaspects of the present disclosure. As shown in FIGS. 33A and 33B, themotivation type widget 3300 b may provide a horizontal bar graphillustrating a user's percentage of time spent in a target glucose rangeper day for a particular duration, such as a seven day period, comparedto a goal percentage of time to be spent in the target glucose range perday.

FIGS. 33C-33E illustrate the example motivation type widget 3300C-3300Ewhich is similar to the motivation type widget 3300B illustrated in FIG.33B but has additional features, in accordance with some example aspectsof the present disclosure. For example, as shown in FIG. 33C, additionalfeatures in the example motivation type widget 3300C may include anindication of a time period when information illustrated in the widgetwas collected. In FIG. 33C, the motivation type widget is showing ahorizontal bar graph for data collected for the user between 7 pm and 11pm. The “track from” time period may be changed to reflect different bargraphs for the user for different data sets from different time periods.

As another example, as shown in FIG. 33D, additional features in theexample motivation type widget 3300D may include a progress bar. Theprogress bar may indicate to the user what percentage of the current daythe user has spent in a target glucose range. The progress bar may beprovided to motivate a user to meet their personalized goal time inrange for the current day. For example, the progress bar may indicate toa user in the morning that their glucose levels have been within theirtarget glucose range for 61% of the day. Where the user has previouslyset a goal to stay within this target glucose range for 70% of the day(e.g., shown as the vertical dashed line), the progress bar may provideinformation to allow a user to know where they are at with respect totheir goal, and in some cases, motivate the user to reach this goal.

In some aspects, as shown in FIG. 33E, additional features in theexample motivation type widget 3300E may include both additionalfeatures illustrated in FIG. 33C and FIG. 33D (e.g., an indication of atime period when information illustrated in the widget was collected anda progress bar).

FIG. 34 illustrates another example motivation type widget 3400, inaccordance with some example aspects of the present disclosure. As shownin FIG. 34, the motivation type widget 3400 may provide a vertical bargraph illustrating a user's percentage of time spent in a target glucoserange per day for a particular duration, such as a seven day period,compared to a goal percentage of time (e.g., 70%) to be spent in thetarget glucose range per day.

FIGS. 35A and 35B illustrate another example motivation type widget 3500b, and its corresponding wireframe 3500 a, in accordance with someexample aspects of the present disclosure. As shown in FIGS. 35A and35B, the motivation type widget 3500 b may provide a vertical graph barillustrating a user's percentage of time spent in a target glucose rangefor today compared to a goal percentage of time to be spent in thetarget glucose range per day. Although not shown, the motivation typewidget 3500 b may, in some aspects, provide a vertical graph barillustrating a user's percentage of time spent in a target glucose rangefor a current week compared to a goal percentage of time to be spent inthe target glucose range per week.

FIG. 36 illustrates another example motivation type widget 3600, inaccordance with some example aspects of the present disclosure. As shownin FIG. 36, the motivation type widget 360 may provide a horizontal bargraph illustrating a number of hours a user spent in a target glucoserange today compared to a goal number of hours to be spent in the targetglucose range per day. Although not shown, the motivation type widget3600 may, in some aspects, provide a vertical graph bar illustrating auser's percentage of time spent in a target glucose range for a currentweek compared to a goal percentage of time to be spent in the targetglucose range per week.

FIG. 37 illustrates another example motivation type widget 3700, inaccordance with some example aspects of the present disclosure. As shownin FIG. 37, the motivation type widget 3700 may provide an indication ofa user's “best day” over a prior defined period of time, such as a priorseven day period. The motivation type widget 3700 may also include asummary of the user's average glucose, the user's percentage of timespent in a target glucose range, and the user's standard deviation forthe indicated “best day”.

FIG. 38 illustrates another example motivation type widget 3800, inaccordance with some example aspects of the present disclosure. As shownin FIG. 38, the motivation type widget 3800 may provide an indication ofa user's “best day” over a prior defined period of time, such as a priorseven day period. The widget may also include a summary of the user'saverage glucose and the user's percentage of time spent in a targetglucose range for the indicated “best day”.

FIG. 39 illustrates another example motivation type widget 3900, inaccordance with some example aspects of the present disclosure. As shownin FIG. 39, the motivation type widget 3900 may provide an indication ofa user's “best day” over a prior seven day period with a summary of theuser's average glucose, the user's standard deviation, and the user'spercentage of time spent in a target glucose range for the indicated“best day”. The motivation type widget 3900 may also include anindication of the user's percentage of time spent in a target glucoserange for the user's “second best day” and “third best day”.

FIG. 40 illustrates another example motivation type widget 4000, inaccordance with some example aspects of the present disclosure. As shownin FIG. 40, the motivation type widget 4000 may provide a comparison ofa user's “best day”, “average day”, and “trouble day” for a previousweek. The motivation type widget 4000 may also include a summary of theuser's average glucose, the user's standard deviation, and the user'spercentage of time spent in a target glucose range for each of theindicated “best day”, “average day”, and “trouble day”.

FIG. 41 illustrates another example motivation type widget 4100, inaccordance with some example aspects of the present disclosure. As shownin FIG. 41, the motivation type widget 4100 may provide a comparison ofa user's “best day”, “average day”, and “trouble day” for a priordefined period of time, such as a prior seven day period, with a summaryof the user's average glucose and the user's percentage of time spent ina target glucose range for each of the indicated “best day”, “averageday”, and “trouble day”.

FIG. 42 illustrates another example motivation type widget 4200, inaccordance with some example aspects of the present disclosure. As shownin FIG. 42, the motivation type widget 4200 may provide a comparison ofa user's percentage of time spent in a target glucose range for a priordefined period of time, such as a prior four week period. The motivationtype widget 4200 may also include a comparison of the user's percentageof time spent in the target glucose range for the prior four week periodto a goal percentage of time to be spent in the target glucose range perday.

FIG. 43 illustrates another example motivation type widget 4300, inaccordance with some example aspects of the present disclosure. As shownin FIG. 43, the motivation type widget 4300 may provide a number ofcontinuous days a user has spent in a target glucose range with anotification to lower or increase the target glucose range. Thenotification may include a text narrative.

FIG. 44 illustrates an example event type widget 4400, in accordancewith some example aspects of the present disclosure. As shown in FIG.44, the event type widget 4400 may provide a summary of a user's glucoselevel at a start of each logged event and the user's glucose level adefine period of time, such as two hours, after the start of the loggedevent.

FIG. 45 illustrates another example event type widget 4500, inaccordance with some example aspects of the present disclosure. As shownin FIG. 45, the event type widget 4500 may provide an indication of auser's glucose level at a start of each logged event and a trend offluctuations in the user's glucose level following each event logged(e.g., one hour after the logged event, two hours after the loggedevent, three hours after the logged event, etc.).

FIG. 46 illustrates another example event type widget 4600, inaccordance with some example aspects of the present disclosure. As shownin FIG. 46, the event type widget 4600 may provide a summary of a user'sglucose level per event per day at the start of each logged event, onehour after the start of the logged event, and two hours after the startof the logged event.

FIGS. 47A and 47B illustrate another example event type widget 4700 b,and its corresponding wireframe 4700 a, in accordance with some exampleaspects of the present disclosure. As shown in FIGS. 47A and 47B, theevent type widget 4700 b may provide trend graphs of a user's glucoselevels, insulin units, carbs logged, steps logged, and sleep logged overa specified period (e.g., 24-hour period from 12:00 AM to 12:00 AM). Asshown, the trend graphs may be represented as bar graphs in someaspects.

FIG. 48 illustrates another example event type widget 4800, inaccordance with some example aspects of the present disclosure. As shownin FIG. 48, the event type widget 4800 may provide information relatedto the user's carb intake over a specified period of time. The carbintake information (i.e., calculated based on a user's input of events,including meals logged by the user) may include a day-by-day bar graphof the user's average carb intake and a day-by-day breakdown of theuser's percentage of time in a target glucose range (shown as target inrange (TIR) in FIG. 48). The TIR percentage illustrated in FIG. 48represents the percentage of time the user is in a particular glucoserange (e.g., a glucose range personalized for the user) for a particularday. Percentage of time in the target glucose range may be provided toassist the user in identifying a correlation, if any, between carbintake and its effect on the user's glucose level.

FIG. 49 illustrates another example event type widget 4900, inaccordance with some example aspects of the present disclosure. As shownin FIG. 49, the event type widget 4900 may provide information relatedto a user's activity over a specified time in combination with glucoselevels of the patient. The activity information (i.e., calculated basedon a user's input or tracker of events, including steps taken by theuser) may include an average number of steps for the current week incomparison to a previous week's average number of steps and the month'saverage number of steps. The event type widget 4900 may also include asummary of the user's average glucose for the current week in comparisonto a previous week's average glucose and the month's average glucose.Further, the event type widget 4900 may also include the user's averagepercentage of time in a target glucose range for the current week incomparison to a previous week's average percentage of time in the targetglucose range and the month's average percentage of time in the targetglucose range.

FIG. 50 illustrates another example event type widget 5000, inaccordance with some example aspects of the present disclosure. As shownin FIG. 50, the event type widget 5000 may provide information relatedto the user's activity over a specified period of time. The activityinformation may include average steps taken by the user per day. Theaverage steps may be presented in a day-by-day bar graph. The bar graphmay also include information about the user's average glucose per day.The comparison may provide insight into activity levels of the patient(e.g., steps taken or not taken) and glucose levels of the user.

FIG. 51 illustrates another example event type widget 5100, inaccordance with some example aspects of the present disclosure. As shownin FIG. 51, the event type widget 5100 may also provide informationrelated to the user's activity over a specified period of time, similarto the event type widget 5000. However, in the event type widget 5100,the activity information (e.g., the user's steps per day) may bepresented in a day-by-day scatter plot as opposed to a day-by-day bargraph. The scatter plot may also include information about the user'saverage glucose per day. The scatter plot may help to provide adifferent view to the user of similar information presented in eventtype widget 5000.

FIG. 52 illustrates another example type widget 5200, in accordance withsome example aspects of the present disclosure. As shown in FIG. 52, thewidget 5200 may provide a summary of a user's average glucose for eachday of the week over a prior defined period of time, such as the lastthirty days (or last week), compared to a goal average glucose per day.As shown, the summary may include both a bar graph and numerical valuesfor average glucose.

FIGS. 53A and 53B illustrate another example type widget 5300 b, and itscorresponding wireframe 5300 a, in accordance with some example aspectsof the present disclosure. As shown in FIG. 53B, the widget 5300 b mayprovide a trend of a user's average glucose levels for a defined periodof time, such as a twenty-four hour period, over a prior duration oftime, such as a prior fourteen day period. In some aspects, the trendgraph may also include a box for a “target range” to provide contextaround average glucose levels measured for the patient, and shown in thetrend graph, in comparison to the user's personalized target range.

FIG. 54 illustrates another example type widget 5400, in accordance withsome example aspects of the present disclosure. As shown in FIG. 54, thewidget 5400 may provide trends of best hours in a defined period oftime, such as a twenty-four hour day, over a prior duration of time,such as a fourteen day period, where a user has a highest percentage oftime spent in a target glucose range. The widget may also include anotification indicating how much time the user must stay in the targetglucose range to reach a goal percentage of time in range. Thenotification may include a text narrative.

FIG. 55 illustrates another example type widget 5500, in accordance withsome example aspects of the present disclosure. As shown in FIG. 55, thewidget 5500 may provide a summary of a user's percentage of time spentin a target glucose range, very high and high glucose ranges, and verylow and low glucose ranges over a defined period of time, such as afourteen day period, the user's average glucose over the defined periodof time, and the user's GMI percentage over the defined period of time.The widget 5500 may also include a trend of the user's average glucoselevels over a prior similar period of time, such as a prior fourteen dayperiod.

It is to be understood that both the foregoing detailed description isexample and explanatory only and is not restrictive. Further featuresand/or variations of widgets for analyte data visualization may beprovided in addition to those set forth above.

According to certain aspects, the widget described herein may becustomizable by a user. For example, a user may customize various targetvalues, ranges, and information to be provided on the one or morewidgets.

FIG. 56 is a flow diagram illustrating example operations 5600 foractivating and using a dashboard with widgets, in accordance with someexample aspects of the present disclosure. A dashboard layer (alsoreferred to as a “unified interest layer” or “dashboard”) may be used tomanage and display widgets. A user may invoke a dashboard, at 5602, inmany ways, including by, but not limited to, hitting a designatedfunction key or key combination, clicking on an icon, selecting acommand from an onscreen menu, or moving an onscreen cursor to adesignated corner of the screen. In response to such user input, thecurrent state of the user interface may be saved, at 5603, the userinterface may be temporarily inactivated (e.g., faded), at 5604, ananimation or effect may be played or presented to introduce thedashboard, at 5605, and the dashboard may be displayed with one or morewidgets, at 5606. Widgets displayed on the dashboard, at 5606, mayinclude one or more of the widgets described previously in FIGS. 19-55.

If applicable, a previous state of the dashboard may be retrieved, sothat the dashboard may be displayed in its previous configuration. Insome example aspects, the user interface and dashboard may be active atthe same time.

At 5607, the user may interact with and/or configure widgets as desired.In some example aspects, the user may move widgets around the screen,and may resize widgets, if applicable. As described previously, somewidgets may be resizable and some may have a fixed size. At 5608, theuser may dismiss the dashboard by invoking a dismissal command, whichmay cause the normal user interface to return or re-present itself tothe display screen. In some example aspects, the dashboard may bedismissed when the user presses a function key or key combination,clicks on a close box or other icon, clicks on negative space within thedashboard (e.g., a space between widgets), or moves an onscreen cursorto a predefined corner of the screen.

In some example aspects, the dashboard can be automatically dismissed(i.e., without user input) after some predetermined period of time or inresponse to a trigger event. At 5609, an animation or other effect maybe played or presented to provide a transition as the dashboard isdismissed. When the dashboard is dismissed, the current configuration orstate of the widgets (e.g., position, size, etc.) may be stored, so thatthe current configuration or state of the widgets may be retrieved thenext time the dashboard is activated. In some example aspects, ananimation or effect can be played or presented when re-introducing theuser interface. At 5610, the user interface may be restored to itsprevious state so that the user may resume interaction with softwareapplications and/or the computer operating system.

In some example aspects, the dashboard may be configurable. The user canselect a number of widgets to be displayed, for example, by dragging thewidgets from a configuration bar (or other user interface element) ontothe dashboard. For example, FIG. 57 illustrates an example userinterface 5700 that may include various widgets, in accordance with someexample aspects of the present disclosure. Widgets selected for displayon the dashboard may include one or more of the widgets describedpreviously in FIGS. 19-55.

There are many ways in which dashboards and widgets may be displayedother than those aspects described herein. For example, dashboards andwidgets may be displayed on any user interface or user interfaceelement, including but not limited to desktops, browser or applicationwindows, menu systems, trays, multi-touch sensitive displays and otherwidgets. Additionally, widgets and dashboards may be displayed on anysurface capable of displaying widgets and dashboards, such asprojections onto surfaces, holograms, surfaces of consumer appliances(e.g., refrigerator doors) and the like.

Additional Considerations

The methods disclosed herein comprise one or more steps or actions forachieving the methods. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover a, b, c,a-b, a-c, b-c, and a-b-c, as well as any combination with multiples ofthe same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b,b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not intended to be limited to theaspects shown herein, but is to be accorded the full scope consistentwith the language of the claims, wherein reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.” Unless specifically statedotherwise, the term “some” refers to one or more. All structural andfunctional equivalents to the elements of the various aspects describedthroughout this disclosure that are known or later come to be known tothose of ordinary skill in the art are expressly incorporated herein byreference and are intended to be encompassed by the claims. Moreover,nothing disclosed herein is intended to be dedicated to the publicregardless of whether such disclosure is explicitly recited in theclaims. No claim element is to be construed under the provisions of 35U.S.C. § 112(f) unless the element is expressly recited using the phrase“means for” or, in the case of a method claim, the element is recitedusing the phrase “step for.”

While various examples of the invention have been described above, itshould be understood that they have been presented by way of exampleonly, and not by way of limitation. Likewise, the various diagrams maydepict an example architectural or other configuration for thedisclosure, which is done to aid in understanding the features andfunctionality that can be included in the disclosure. The disclosure isnot restricted to the illustrated example architectures orconfigurations, but can be implemented using a variety of alternativearchitectures and configurations. Additionally, although the disclosureis described above in terms of various example examples and aspects, itshould be understood that the various features and functionalitydescribed in one or more of the individual examples are not limited intheir applicability to the particular example with which they aredescribed. They instead can be applied, alone or in some combination, toone or more of the other examples of the disclosure, whether or not suchexamples are described, and whether or not such features are presentedas being a part of a described example. Thus the breadth and scope ofthe present disclosure should not be limited by any of theabove-described example examples.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein.

Terms and phrases used in this application, and variations thereof,especially in the appended claims, unless otherwise expressly stated,should be construed as open ended as opposed to limiting. As examples ofthe foregoing, the term ‘including’ should be read to mean ‘including,without limitation,’ ‘including but not limited to,’ or the like; theterm ‘comprising’ as used herein is synonymous with ‘including,’‘containing,’ or ‘characterized by,’ and is inclusive or open-ended anddoes not exclude additional, unrecited elements or method steps; theterm ‘having’ should be interpreted as ‘having at least;’ the term‘includes’ should be interpreted as ‘includes but is not limited to;’the term ‘example’ is used to provide example instances of the item indiscussion, not an exhaustive or limiting list thereof, adjectives suchas ‘known’, ‘normal’, ‘standard’, and terms of similar meaning shouldnot be construed as limiting the item described to a given time periodor to an item available as of a given time, but instead should be readto encompass known, normal, or standard technologies that may beavailable or known now or at any time in the future; and use of termslike ‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular example of the invention. Likewise, a group of items linkedwith the conjunction ‘and’ should not be read as requiring that each andevery one of those items be present in the grouping, but rather shouldbe read as ‘and/or’ unless expressly stated otherwise. Similarly, agroup of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

The term “comprising as used herein is synonymous with “including,”“containing,” or “characterized by” and is inclusive or open-ended anddoes not exclude additional, unrecited elements or method steps.

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. Therefore, the descriptionand examples should not be construed as limiting the scope of theinvention to the specific examples and examples described herein, butrather to also cover all modification and alternatives coming with thetrue scope and spirit of the invention.

1. A method for generating a user interface view associated with sensordata representative of analyte levels in a host, comprising: accessingsensor data including a plurality of analyte readings associated withthe host during a plurality of analysis time periods in a current week,where each analyte reading is indicative of an analyte level of the hostat a respective time; determining an average analyte level of the hostfor the current week; generating a performance report including theaverage analyte level of the host for a first time period and acomparison of the average analyte level of the host for the first timeperiod to average analyte levels of the host for at least two previoustime periods of similar duration; generating a user interface view ofthe performance report; and providing the user interface view of theperformance report for display.
 2. The method of claim 1, wherein theperformance report further includes per-day average analyte levels ofthe host, per-day percentages of analyte levels of the host at one ormore analyte level ranges, or a combination thereof.
 3. A method forgenerating a user interface view associated with sensor datarepresentative of analyte levels in a host, comprising: accessing firstdata associated with the analyte levels of a host, the first data beingassociated with a first time period; analyzing the first data togenerate a first one or more user interface views associated with thefirst data for display on one or more widgets; providing the first oneor more user interface views for display on the one or more widgets; andautomatically updating the first one or more user interface views fordisplay on the one or more widgets, wherein the automatically updatingincludes: accessing second data associated with the analyte levels ofthe host, the second data being associated with a second time period;analyzing the second data to generate a second one or more userinterface views associated with the second data for display on the oneor more widgets; and providing the second one or more user interfaceviews for display on the one or more widgets.
 4. The method of claim 3,wherein the first one or more user interface views for display on one ormore widgets comprise: a summary of a percentage of time the analytelevels of the first data were within a target analyte range associatedwith the first time period; and an average analyte level of the hostbased on the first data.
 5. The method of claim 3, wherein the first oneor more user interface views for display on one or more widgetscomprise: a summary of an average analyte level of the host based on thefirst data; a standard deviation of the analyte levels of the firstdata; and an indication of a current state of analyte management by thehost based on the first data.
 6. The method of claim 3, wherein thefirst one or more user interface views for display on one or morewidgets comprise: a summary of a percentage of time the analyte levelsof the first data were within a target analyte range associated with thefirst time period; an average analyte level of the host based on thefirst data; and a standard deviation of the analyte levels of the firstdata.
 7. The method of claim 3, wherein the first one or more userinterface views for display on one or more widgets comprise: a summaryof a percentage of time the analyte levels of the first data were withina target analyte range associated with the first time period; an averageanalyte level of the host based on the first data; and an indication ofa current state of analyte management by the host based on the firstdata.
 8. The method of claim 3, wherein the first one or more userinterface views for display on one or more widgets comprise: a summaryof a percentage of time the analyte levels of the first data were withina target analyte range associated with the first time period, a veryhigh analyte range associated with the first time period, a high analyterange associated with the first time period, a low analyte rangeassociated with the first time period, and a very low analyte rangeassociated with the first time period.
 9. The method of claim 3, whereinthe first one or more user interface views for display on one or morewidgets comprise: a summary of a percentage of time the analyte levelsof the first data were within a target analyte range associated with thefirst time period, a very high analyte range associated with the firsttime period, a high analyte range associated with the first time period,a low analyte range associated with the first time period, and a verylow analyte range associated with the first time period; and apercentage of change the summary of the percentage of time the analytelevels of the first data were within a target, a very high, a high, alow, and a very low analyte range associated with the first time periodchanged compared to a summary of the percentage of time the analytelevels of the host for a third time period were within a target, a veryhigh, a high, a low, and a very low analyte range associated with thethird time period.
 10. The method of claim 3, wherein the first one ormore user interface views for display on one or more widgets comprise: asummary of a percentage of time the analyte levels of the first datawere within a target analyte range associated with the first timeperiod, a very high analyte range associated with the first time period,a high analyte range associated with the first time period, a lowanalyte range associated with the first time period, and a very lowanalyte range associated with the first time period; an average analytelevel of the host based on the first data; and an indication of acurrent state of analyte management by the host based on the first data.11. The method of claim 3, wherein the first one or more user interfaceviews for display on one or more widgets comprise: a summary of apercentage of time the analyte levels of the first data were within atarget analyte range associated with the first time period, a very highanalyte range associated with the first time period, a high analyterange associated with the first time period, a low analyte rangeassociated with the first time period, and a very low analyte rangeassociated with the first time period; a percentage the summary of thepercentage of time the analyte levels of the first data were within atarget, a very high, a high, a low, and a very low analyte rangeassociated with the first time period changed compared to a summary ofthe percentage of time the analyte levels of the host for a third timeperiod were within a target, a very high, a high, a low, and a very lowanalyte range associated with the third time period; an average analytelevel of the host based on the first data; an indication of a currentstate of analyte management by the host based on the first data; and anindication of a period of time during the first time period where theanalyte levels of the first data were within the high and very highrange associated with the first time period or the low and very lowrange associated with the first time period.
 12. The method of claim 3,wherein the first one or more user interface views for display on one ormore widgets comprise: a comparison, wherein the comparison compares: apercentage of time the analyte levels of the first data were within atarget analyte range associated with the first time period; a percentageof time the analyte levels of the host associated with a third timeperiod were within a target analyte range associated with the third timeperiod; a percentage of time the analyte levels of the host associatedwith a fourth time period were within a target analyte range associatedwith the fourth time period, wherein the fourth time period is longerthan the first time period and includes the first time period; and apercentage of time the analyte levels of the host associated with afifth time period were within a target analyte range associated with thefifth time period, wherein the fifth time period is longer than thefourth time period and includes the first time period.
 13. The method ofclaim 3, wherein the first one or more user interface views for displayon one or more widgets comprise: a comparison, wherein the comparisoncompares: a percentage of time the analyte levels of the first data werewithin a target analyte range associated with the first time period; apercentage of time the analyte levels of the host associated with athird time period were within a target analyte range associated with thethird time period, wherein the third time period is longer than thefirst time period and includes the first time period; and a percentageof time the analyte levels of the host associated with a fourth timeperiod were within a target analyte range associated with the fourthtime period, wherein the fourth time period is longer than the thirdtime period and includes the third time period; a first average analytelevel of the host based on the analyte levels corresponding to the firsttime period, a second average analyte level of the host based on theanalyte levels corresponding to the third time period, and a thirdaverage analyte level of the host based on the analyte levelscorresponding to the fourth time period; and a first standard deviationof the analyte levels corresponding to the first time period, a secondstandard deviation of the analyte levels corresponding to the third timeperiod, and a third standard deviation of the analyte levelscorresponding to the fourth time period.
 14. The method of claim 3,wherein the first one or more user interface views for display on one ormore widgets comprise a summary of a percentage of time, a number ofminutes, and a number of hours the analyte levels of the first data werewithin a target analyte range associated with the first time period, avery high analyte range associated with the first time period, a highanalyte range associated with the first time period, a low analyte rangeassociated with the first time period, and a very low analyte rangeassociated with the first time period.
 15. The method of claim 3,wherein the first one or more user interface views for display on one ormore widgets comprise a summary of a number of minutes and a number ofhours the analyte levels of the first data were within a target analyterange associated with the first time period, a high analyte rangeassociated with the first time period, and a low analyte rangeassociated with the first time period.
 16. The method of claim 3,wherein the first one or more user interface views for display on one ormore widgets comprise a summary of a number of high analyte events and anumber of low analyte events the host participated in during the firsttime period, based on the first data.
 17. The method of claim 3, whereinthe first one or more user interface views for display on one or morewidgets comprise a summary of the analyte levels of the first datapresented as a trend in a graph over a second time period with a line ofbest fit.
 18. The method of claim 3, wherein: the analyte levels of thefirst data are broken into multiple equal data sets with correspondinganalyte levels; and the first one or more user interface views fordisplay on one or more widgets comprise a percentage of time the analytelevels corresponding to each of the multiple equal data sets were withina target analyte range associated with the first time period presentedin a horizontal bar graph and compared to a goal percentage of time theanalyte levels are desired to be within a target analyte rangeassociated with the first time period.
 19. The method of claim 3,wherein: the analyte levels of the first data are broken into multipleequal data sets with corresponding analyte levels; and the first one ormore user interface views for display on one or more widgets comprise apercentage of time the analyte levels corresponding to each of themultiple equal data sets were within a target analyte range associatedwith the first time period presented in a vertical bar graph andcompared to a goal percentage of time the analyte levels are desired tobe within a target analyte range associated with the first time period.20. The method of claim 3, wherein the first one or more user interfaceviews for display on one or more widgets comprise: a summary of apercentage of time a subset of the analyte levels the first data werewithin a target analyte range associated with the first time periodpresented in a vertical bar and compared to a goal percentage of timethe analyte levels are desired to be within the target analyte rangeassociated with the first time period.